作为一名长期写Java的工程师，同时跟踪大模型发展，我每天都在和各种代码模型打交道。模型迭代速度太快，今天还在榜首的基准分，下个月可能就被刷新。这篇文章把当下主流14款模型的Java编码能力做一次系统性拆解，所有数据均来自各厂商官方发布与第三方独立评测（BenchLM、Continuum、LiveCodeBench、DeepSWE等），不掺水、不脑补。
一、先搞清楚：代码能力到底怎么测
谈模型编码能力，不能只看一个分数。不同基准测的是完全不同的东西，混在一起比没有意义。目前行业内公认的代码能力评测主要分四类：
第一类，算法与代码生成。 代表是LiveCodeBench（LCB），题目来自真实竞赛平台且持续更新，防止数据污染。这个基准反映模型解算法题、写函数级代码的能力。注意：LCB不同快照难度差异极大，2026年上半年快照能到90+，下半年新快照普遍掉到50左右，跨快照的分数不能直接横比。
第二类，仓库级软件工程。 代表是SWE-bench Verified和SWE-bench Pro。给模型一个真实GitHub仓库的issue，要求它定位代码、修改并通过测试。这是最接近日常开发的基准，但也最难。SWE-bench Verified是通过严格验证的子集，SWE-bench Pro是更难的专业版。
第三类，多语言软件工程。 代表是SWE-bench Multilingual，在Python之外加入了Java、JavaScript、Go等语言的真实仓库任务。这个基准对Java开发者参考价值最高，因为它直接包含Java工程题。
第四类，Agentic长程任务。 代表是DeepSWE v1.1、Terminal-Bench、StepCodeBench、ProgramBench。这类基准要求模型在终端环境中自主完成多步操作——写代码、跑命令、看报错、改bug、再验证。DeepSWE测长程软件工程，Terminal-Bench测终端操作能力，StepCodeBench覆盖553个仓库、33种语言（含Java），ProgramBench测日常编程全流程。
这里有两个关键坑必须提醒：Terminal-Bench 2.1和4.0不是一个难度，4.0更难，所以V4.1-Flash的90.6（2.1）和Opus 5.5的66.4（4.0）不能直接比谁强。LiveCodeBench的快照同理，93.5和54.2是不同时间点的题库。下文所有排名都基于同基准横向对比，不做跨版本的粗暴相加。
二、核心数据一览
先看一张全量对比表，把14款模型的关键分数摊开。空格表示该模型未公开发布此基准分数。
数据来源：Anthropic系统卡、OpenAI发布页、DeepSeek官方更新日志、Qwen官方博客、Kimi技术博客、MiniMax博客、StepFun官方页、腾讯混元发布页、智谱AI、BenchLM、Continuum、LiveCodeBench官方榜。
三、第一梯队：闭源三强，断层领先
这三款是当前编码能力的天花板，在多个核心基准上包揽前三，和后面的模型拉开了明显差距。
1. Claude Opus 5.5：综合之王
Anthropic在2026年10月推出的新旗舰，定位是"Fable级能力、更低成本"。它的SWE-bench Multilingual拿到93.9%，是所有模型里最高的——而这个基准直接包含Java仓库任务，对Java开发者来说这就是硬通货。SWE-bench Pro 89.9%同样全场第一，DeepSWE 74.2%排在第三。
Opus 5.5的特点是均衡。它不是某一项特别炸，而是软件工程全链路都强：理解需求、定位代码、修改实现、跑测试验证，每一步都不掉链子。Anthropic官方数据里它的GDPval-AA v2.1达到1846 Elo，高于Fable 5.1的1735，说明在真实职业任务上它的完成度更高。
对Java开发者的实际意义：如果你在做大型Spring Boot项目的重构、多模块依赖调整、复杂业务逻辑迁移，Opus 5.5是目前最稳的选择。它能理解Java的强类型约束、注解配置、泛型边界这些容易出错的点，生成的代码编译通过率和测试通过率都更高。
2. Claude Fable 5.1：推理与自验证的偏执狂
Fable 5.1是Anthropic的推理旗舰，2026年9月发布。它最突出的能力是自己验证自己的代码。Anthropic官方提到它在CursorBench 3.2上拿到73.4%，是Anthropic史上最高，原因就是它会反复检查自己写的代码是否真的能跑通。
关键分数：SWE Multilingual 89.1%（全场第二）、LiveCodeBench 90.5、SWE Pro 81.2%、DeepSWE 67.4%。
Fable系列的设计哲学是"慢工出细活"。它会花更多token去思考和验证，所以在困难的算法题和需要反复调试的工程任务上表现突出。代价是速度慢、成本高。
我的实际使用感受：用Fable 5.1写复杂的Java并发代码、JVM调优脚本、或者需要精确理解泛型擦除的工具类，它的出错率明显比其他模型低。但如果只是写一个简单的Controller，它的思考过程反而显得啰嗦。
3. GPT-6 Astra：Agentic编程新王
OpenAI的新旗舰，官方直接称它为"迄今为止最适合软件工程的模型"。它在Agentic编程基准上的表现令人咋舌：ProgramBench 85.4（全场最高，高于Claude Opus 5的82.3和Step-5的80.5），StepCodeBench 61.0（含Java的多语言基准，全场第二，仅次于Opus 5的63.9），FrontierSWE v2 65.5%（反超Opus 5.5的62.3%）。
Astra的强项是长程任务和代码库理解。OpenAI内部的数据库迁移基准上，Astra拿到57.9%，高于Fable 5.1的55.8%和GPT-5.6 Sol的37.3%。这说明它在处理"读旧代码→理解逻辑→迁移到新框架→验证"这种典型Java老系统改造场景时，能力很强。
它的短板是目前公开发布的SWE Multilingual、SWE Verified等传统基准分数较少，所以和Opus 5.5、Fable 5.1的对比主要靠Agentic基准。但从已有数据看，Astra在实战工程场景的竞争力极强。
四、第二梯队：开源与国产旗舰，迫近但未超越
这一档的模型在某些单点基准上能挑战第一梯队，但综合软件工程能力仍有差距。好消息是，它们都是开源或价格友好的，性价比突出。
4. DeepSeek-V4-Pro：开源编码的标杆
DeepSeek-V4-Pro是开源模型里编码能力的天花板。LiveCodeBench 93.5（旧快照）、SWE-bench Verified 80.6%、SWE Multilingual 76.2%，这三个分数放在一起，开源阵营无出其右。
它的DeepSWE是62.7%，看起来不如后面几款，但要注意DeepSWE是2026年下半年才火起来的基准，V4-Pro发布较早，用的是早期配置。V4.1-Flash发布后在同样的DeepSWE上拿到77.0%，说明DeepSeek的后训练优化空间很大。
V4-Pro的优势是全面且开源可部署。对企业来说，如果不能把代码发给闭源API，V4-Pro是目前自托管编码模型的最优解。Java方面，它对Spring、MyBatis、Dubbo等主流框架的代码生成质量很高，因为训练语料里中文Java工程占比不小。
5. DeepSeek-V4.1-Flash：以Flash之身干翻旗舰
这是2026年9月发布的新款，最让人意外的模型。它作为Flash版本（速度快、成本低），在DeepSWE v1.1上拿到77.0%，全场最高，超过了Opus 5.5的74.2%和Space-Bunny的73.7%。在2026年8月的LiveCodeBench新快照上，它拿到54.2，也超过了V4-Pro的52.8。
V4.1-Flash采用了新的非对称编解码架构和更大规模的强化学习后训练。它的定位很明确：用Flash的成本，干Pro的活。对Java开发者来说，这意味着你可以用更低的API价格获得接近旗舰的编码质量，尤其是长程工程任务。
我的建议：日常Java开发用V4.1-Flash完全够用，性价比极高。只有在处理超复杂架构设计时，才需要切到Opus 5.5或V4-Pro。
6. Qwen3.8-Max：终端操作的隐形冠军
阿里通义千问的旗舰。它的Terminal-Bench 2.1拿到86.6，QwenSWE 80.7，LiveCodeBench v6 92.6。三个分数都在第一梯队边缘。
Qwen3.8-Max的特点是终端操作和工程化能力强。Qwen官方博客里展示过它连续10多天自主编码、构建自我进化工具链的案例，这种长程执行能力在Java项目的构建、部署、CI/CD脚本生成上很有价值。
它的Java生态理解也很到位，对Maven/Gradle依赖管理、Spring Boot自动配置、JVM参数调优这些工程细节的处理比较成熟。
五、第三梯队：各怀绝技的中生代
这一档的模型在某个特定维度有亮点，但综合能力不如前两档。胜在各有特色，适合特定场景。
7. Space-Bunny（MiniMax M3.1 Flash）：DeepSWE的黑马
MiniMax在2026年10月19日发布的M3.1 Flash，官方确认之前匿名领跑OpenRouter的"Space Bunny"就是它的早期版本。428B MoE架构，价格极低（$0.15/M输入token）。
它的DeepSWE v1.1拿到73.7%，排在全场第四，仅次于V4.1-Flash、Opus 5.5、V4-Pro(74.2算第三)。在SWE Refore Bench（代码修复专项）上拿到23.0，是DeepSeek同基准分数的两倍以上。
Space-Bunny的特点是"便宜又能打"。作为Flash模型，它在软件工程基准上的表现已经超过很多Pro级模型。对预算敏感但需要工程编码能力的团队，这是一个极具性价比的选择。
8. Kimi K3：开源巨无霸的终端统治力
月之暗面的开源旗舰，2779.9B总参数的3T级MoE模型。它的Terminal-Bench 2.1拿到88.3，仅次于V4.1-Flash的90.6，高于Qwen3.8-Max的86.6。DeepSWE 67.3%，ProgramBench 77.8。
Kimi K3的核心优势是开源+大参数+长上下文（1M）。它在终端操作和长程任务上的表现接近闭源旗舰，而且权重完全开源，可以本地部署。对有数据合规要求、又需要强编码能力的Java团队，Kimi K3是V4-Pro之外的另一个选择。
需要注意的是，Kimi K3的激活参数也很大，本地部署对显存要求极高（量化后仍需约1.6TB显存），普通开发者很难跑得动，主要面向有算力集群的企业。
9. Step-5-Preview：多语言SE的专精选手
阶跃星辰2026年9月发布的旗舰，600B MoE，采用"窄而深"架构（92层transformer block）。它的StepCodeBench拿到49.0，这个基准覆盖553个仓库、33种编程语言（明确包含Java），Step-5在这个基准上领先GLM-5.3的40.2和Kimi K3的43.9。ProgramBench 80.5也接近Claude Opus 5的82.3。
Step-5的设计思路是用更少的激活参数做更深的推理。它在真实多语言软件工程场景的表现很扎实，特别是涉及Java、Go等静态类型语言的仓库级任务。
10. Hy4 preview：多语言Java的直接高分
腾讯混元2026年8月发布的开源旗舰，770B MoE（49B激活）。它最亮眼的是SWE-bench Multilingual 82.9%，这个含Java的多语言基准分数仅次于Opus 5.5和Fable 5.1，高于V4-Pro的76.2。
Hy4的Terminal-Bench 2.1是85.4，DeepSWE 64.3%。腾讯内部163位专家、203项工程任务的盲评中，Hy4拿到2.99/4.00，领先GLM-5.3的2.92和Kimi K3的2.94。
对Java开发者来说，Hy4的SWE Multilingual高分是一个很实在的信号，说明它在Java多语言工程任务上确实有竞争力。不过它的纯算法基准（BenchLM coding点估计56.7）相对保守，说明它更偏工程而非算法竞赛。
六、第四、五梯队：够用但不惊艳
11. Qwen3.7-Plus
作为Qwen的非旗舰档，它的LiveCodeBench v6 89.6和SWE-bench Verified 77.7%已经很不错。日常Java CRUD开发、接口编写、简单bug修复完全够用，价格也比Max便宜不少。适合作为日常开发的主力模型，遇到复杂任务再切到Max或闭源旗舰。
12. GLM-5.3
智谱的旗舰，编程能力较GLM-5.2有大幅提升（Z.ai Code Bench +50%）。DeepSWE 66.9%、StepCodeBench 40.2。但在和2026年下半年新模型的对比中，它已经被Kimi K3、Step-5、Space-Bunny等在多个基准上反超。胜在开源和生态成熟，作为备选方案没问题。
13. Seed-2.1-Turbo
字节跳动豆包的模型，LiveCodeBench v6 66.1、SWE-bench Verified 56.8%。它的端到端Coding流程在开发者众测中口碑不错，但纯基准分数确实低于上面的模型。Java复杂任务的稳定性还有提升空间。
14. DeepSeek-V4-Flash
这是DeepSeek V4系列的初代Flash，2026年8月的LiveCodeBench新快照只有48.7，明显弱于同系列的V4-Pro和V4.1-Flash。适合简单的Java代码片段生成，不推荐用于复杂工程任务。如果预算有限，建议直接上V4.1-Flash，体验差距很大。
七、Java专项：哪些基准真正决定Java编码能力
很多Java开发者问我："这些基准里Python占比那么高，能反映Java能力吗？"这个问题问得对。我把和Java最相关的基准单独拎出来说。
SWE-bench Multilingual是Java信号最强的基准。 它在Python之外加入了Java、JS、Go等语言的真实仓库issue修复任务。Java开发者看这个分数最有参考价值。Opus 5.5的93.9%、Fable 5.1的89.1%、Hy4的82.9%、V4-Pro的76.2%，这个排序基本就是Java多语言工程能力的排序。
StepCodeBench是另一个含Java的硬基准。 它由StepFun构建，覆盖33种编程语言，其中明确包含Java。Opus 5以63.9领跑，GPT-6 Astra 61.0紧随其后，Step-5 49.0，Kimi K3 43.9，GLM-5.3 40.2。这个基准更侧重真实仓库的多语言任务，对Java项目的参考价值很高。
DeepSWE虽然主要是Python仓库，但它测的是长程工程能力。 长程能力是通用的——一个能在Python仓库里自主完成"理解需求→改代码→跑测试→修bug"全流程的模型，迁移到Java仓库时，这套工程方法论依然有效。所以DeepSWE高分模型（V4.1-Flash 77.0、Opus 5.5 74.2、Space-Bunny 73.7）在Java长程任务上也不会差。
还有一个容易被忽略的点：Java的强类型和编译期检查特性。Python代码写错了可能运行时才报错，Java代码写错了编译就过不了。这意味着对Java来说，模型生成代码的"一次编译通过率"比"逻辑正确率"更关键。Opus 5.5和Fable 5.1在这方面表现更好，因为它们的代码自验证能力更强，能在生成后自行检查类型匹配、泛型边界、注解参数等编译期问题。
八、实战选型：不同Java场景该用谁
理论说再多，最后还是要落到"我到底该用哪个"。我按Java开发者的典型场景给一个直接的选型建议。
场景一：大型Spring Boot项目重构/多模块迁移。 选Opus 5.5。它的SWE Multilingual和SWE Pro都是全场最高，对Java工程的理解最全面。复杂的依赖调整、配置迁移、接口兼容性处理，它的出错率最低。
场景二：复杂算法/数据结构Java实现。 选Fable 5.1或V4-Pro。Fable 5.1的LCB 90.5和自验证能力适合写需要精确边界处理的算法代码。V4-Pro的LCB 93.5（旧快照）算法能力也极强，而且开源。
场景三：日常CRUD开发/接口编写。 选V4.1-Flash或Qwen3.7-Plus。V4.1-Flash的性价比无敌，Qwen3.7-Plus的SWE Verified 77.7%也足够应对日常开发。这两个模型在常规Java开发场景下和旗舰的体验差距不大。
场景四：老Java系统改造/数据库迁移。 选GPT-6 Astra。它的数据库迁移基准57.9%全场最高，FrontierSWE和StepCodeBench也很强。处理"读懂6年前的老代码→迁移到新框架"这种任务，Astra的长程理解能力是优势。
场景五：数据合规要求/必须本地部署。 选V4-Pro或Kimi K3。V4-Pro综合更强，Kimi K3终端操作更猛，但部署成本也更高（需大量显存）。如果算力有限，V4-Pro是更现实的选择。
场景六：预算敏感但需要工程能力。 选Space-Bunny或V4.1-Flash。Space-Bunny的DeepSWE 73.7%和$0.15/M的价格几乎没有对手。V4.1-Flash的综合能力稍强，价格也很低。这两个是性价比首选。
九、评分与最终排序
基于上述所有基准数据，结合Java编码的五个维度（算法实现30%、Java语言与生态20%、软件工程25%、调试修复15%、代码质量10%），给出综合评分（满分100）：
十、几个容易被忽视的真相
写这篇文章的过程中，有几个点我觉得必须单独拿出来说，因为很多人在选模型时会踩坑。
第一，别只看一个分数。 有些模型在LCB上很高，但SWE Multilingual一般，说明它算法题厉害但工程能力一般。Java开发90%的时间是在写工程代码而非算法题，所以SWE Multilingual、DeepSWE、StepCodeBench这些工程基准对你更重要。
第二，快照和版本比分数更重要。 同一个模型在LCB旧快照能拿93，新快照只有52，不是模型变弱了，是题库变难了。Terminal-Bench 2.1和4.0同理。比较时一定要看是哪个版本的基准。
第三，厂商自报分数要打折扣。 很多高分是厂商在自己的harness、最优配置下跑出来的，第三方独立复测往往会低一些。比如Hy4的BenchLM coding点估计只有56.7，和厂商数据有差距。参考时优先看独立第三方（BenchLM、Continuum、LiveCodeBench官方）的数据。
第四，Java的编译期特性让"慢模型"更有优势。 Fable 5.1、Opus 5.5这类会花时间自验证的模型，在Java上的优势比在Python上更大。因为Java编译不过就等于零，多花点token检查类型和注解是值得的。
第五，开源模型的差距在快速缩小。 一年前开源和闭源的编码差距还很大，现在V4.1-Flash的DeepSWE已经超过了Opus 5.5。V4-Pro、Kimi K3也在多个基准上迫近闭源旗舰。如果趋势持续，开源模型在Java编码上追平闭源只是时间问题。
十一、the end,not ending
模型迭代太快，这篇文章里的分数可能三个月后就过时了。但评测方法论不会过时：看Java能力就盯SWE Multilingual和StepCodeBench，看工程能力就看DeepSWE和SWE Pro，看算法就看LCB。把这几个基准吃透，任何新模型出来你都能快速定位它的水平。
对Java开发者来说，当下最务实的策略是：日常开发用V4.1-Flash或Space-Bunny这种高性价比模型，遇到复杂重构或老系统迁移再切到Opus 5.5或GPT-6 Astra。开源合规需求就上V4-Pro。不用追求"最强"，追求"最合适"。毕竟，模型只是工具，写出能跑、能维护、能扩展的Java代码，最终靠的还是你对业务和技术的理解。模型能帮你提速，但不能替你思考。
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As an engineer with a long writing on Java, and following the development of LLM, I deal with various code models every day. Models are overloaded and are still at the top of the list today and may be updated next month. This article systematically disassemblys the Java coding capabilities of the current 14 mainstream models, all of which are derived from independent assessments of the manufacturers ' official publish and third parties (BenchLM, Continuum, LiveCodeBench, DeepSWE, etc.), without water or brainwashing.
I. DEFINITIONS: HOW THE COORDINATION CAPACITY IS GUESSED
When it comes to model coding capabilities, it is not possible to see only one fraction. The different benchmarks are completely different things, and it makes no sense to mix them. At present, there are four main types of assessments of recognized code capabilities within the industry:
Category one, algorithms and code generation. The representative is LiveCodeBench (LCB), which comes from a real competitive platform and is continuously updated to prevent data pollution. This baseline reflects the ability of the model to solve algorithms and write function-level codes. Note: The difficulty of different LCB snapshots varies greatly, ranging from 90+ in the first half of 2026 to around 50 in the second half of the year, with no direct cross-references.
Category II, warehouse-level software engineering. Representatives are SWE-Bench Verified and SWE-Bench Pro. Give the model a real GitHub repository-issue that requires it to locate the code, modify it and pass the test. This is the closest, but also the hardest, benchmark to daily development. SWE-bench Verified is a highly authenticated subset, and SWE-bench Pro is a more difficult professional version.
Category III, multilingual software engineering. The representative, SWE-Bench Multilingual, added to Python the real warehouse tasks in Java, JavaScript and Go languages. This benchmark is of the highest reference value to Java developers because it directly includes Java Project.
Category IV, Agentic long-range mission. Representatives are DeepSWE v1.1, Terminal-Bench, StepCodeBench, ProgramBench. Such baselines require models to perform multiple-step operations — code writing, running commands, error reporting, bug modification, revalidation — autonomously in the terminal environment. DeepSWE long-range survey software project, Terminal-Bench terminal operational capability, StepCodeBench covering 553 warehouses, 33 languages (including Java) and ProgramBench measuring the full daily programming process.
There are two key pits that must be reminded: Terminal-Bench 2.1 and 4.0 are not difficult and 4.0, so that V4.1-Flash ' s 90.6 (2.1) and Opus 5.5 ' s 66.4 (4.0) cannot be directly superior to anyone. LiveCodeBench's snapshots are the same, 93.5 and 54.2 are the repositories of different points of time. All rankings below are based on a horizontal comparison with the benchmark and are not added to the cross-version.
II. Overview of core data
First, a full comparison table spreads the key scores of the 14 models. Space indicates that the model does not disclose this base score of publish.
Data sources: Anthropic system card, OpenAIpublish page, DeepSeek official update journal, Qwen official blog, Kimi technical blog, Mini Max blog, StepFun official page, Tencent mixed publish page, Zhipu AI, BenchLM, Continum, LiveCodeBench official list.
iii. First Platoon: The three main sources are closed and the fault leads.
These are the ceilings of current coding capabilities, covering the top three on a number of core benchmarks and opening a clear gap with the latter model.
Claude Opus 5.5: King of Integration
Anthropic launched a new flagship in October 2026, located "Fable-class capability, lower cost". Its SWE-bench Multilingual received 93.9 per cent, the highest of all models — a benchmark that directly included Java warehouse tasks, which was hard currency for Java developers. SWE-bench Pro 89.9 per cent is also the first and DeepSWE 74.2 per cent is the third.
Opus 5.5 is characterized by balance. It is not a particular explosion, but a full chain of software engineering is strong: understanding needs, positioning codes, modification realization, running test validation, and never drops a chain. In Anthropic official data, its gross domestic product (GDP) val-AA v. 2.1 amounted to 1846 Elo, up from 1735 in Fable 5.1, indicating a higher degree of performance in real professional tasks.
For Java developers: Opus 5.5 is currently the most stable option if you are doing the re-engineering of a large Spring Boot project, multimodule reliance on adjustments, complex business logic migration. It understands Java ' s strong type of constraints, anotation configuration, and trans-borders, which are prone to errors, and the resulting pass rate of code codification and test pass.
Claude Fable 5.1: Logic and self-proven paranoia
Fable 5.1 is Anthropic's reasoning flagship, publish, September 2026. Its most prominent ability is to authenticate its own code. Anthropic official mentions that it obtained 73.4 per cent of Cursor Bench 3.2, which is the highest in the history of Anthropic, because it double-checks whether the code it wrote really works.
Key scores: SWE Multilingual 89.1% (part two), LiveCodeBench 90.5, SWE Pro 81.2%, DeepSWE 67.4%.
The design philosophy of the Fable series is "slow work." It spends more token on reflection and validation, so it stands out on difficult algorithms and on engineering tasks that need to be repeated. The costs are slow and costly.
My actual usage experience: writing complex Java simultaneous code with Fable 5.1, JVM fine scripts, or tools that need to be accurately understood as panoramic wipes, has a significantly lower error rate than other models. But if it's just a simple Controller, it's a ludicrous process.
3. GPT-6 Astra: Agentic programming king
Open AI's new flagship, officially called "the best model for software engineering to date". Its performance on the Agenic programming benchmark was impressive: ProgramBech 85.4 (the highest in the field, 82.3 above Claude Opus 5 and 80.5 from Step-5, StepCodeBench 61.0 (multilingual benchmark with Java, second in the field, after 63.9), FrontierSWE v. 65.5 per cent (62.3 per cent against Opus 5.5).
Astra's strength is long-range task and code library understanding. On the database migration benchmark within Open AI, Astra received 57.9 per cent more than 55.8 per cent of Fable 5.1 and 37.3 per cent of GPT-5.6 Sol. This suggests that it is highly capable of handling a typical Java-old system retrofitting scenario, such as "reading old code and understanding logic migration to the new frame."
Its short board is the traditional reference scores of SWE Multilingual and SWE Verified, which are currently open to publish, so the comparison with Opus 5.5 and Fable 5.1 is largely based on the Agenic benchmark. However, according to available data, Astra is extremely competitive in the field.
IV. Second Platoon: Open Source and Flagship of National Production, approaching but not exceeding
The model for this tranche can challenge the first tier on some single point benchmarks, but there is still a gap in integrated software engineering capabilities. The good news is that they are open-source or price-friendly, with high value for money.
DeepSeek-V4-Pro: A pole for open source code
DeepSeek-V4-Pro is the ceiling with coding capabilities in open source models. LiveCodeBench 93.5 (old snapshot), SWE-bench Verified 80.6%, SWE Multilingual 76.2%, all three scores taken together, with no right to the open-source camp.
Its DeepSWE is 62.7 per cent, which does not look as good as the latter paragraphs, but it is important to note that DeepSWE was the baseline for fire only in the second half of 2026, with V4-Propublish earlier, using early configuration. V4.1-Flashpublish was then given 77.0% on the same DeepSWE, indicating that DeepSeek has a great post-training optimization.
V4-Pro has the advantage of being comprehensive and open-source deployable. V4-Pro is the best solution to the current self-hosted code model if the code cannot be sent to the closed source API. Java, for its part, generates high-quality codes for mainstream frameworks such as Spring, MyBatis and Dubbo, as the Chinese language of training is well represented in the Java project.
DeepSeek-V4.1-Flash: Flagships in Flash
This is the most surprising model of publish in September 2026. It received 77.0 per cent of DeepSWE v1.1 as a Flash version (quick and low-cost), with the highest total of 74.2 per cent of Opus 5.5 and 73.7 per cent of Space-Bunny. In a new snapshot of LiveCodeBench in August 2026, it received 54.2 or 52.8 for V4-Pro.
V4.1-Flash introduced a new asymmetric decoded architecture and a larger scale of intensive post-learning training. It is well positioned: to do Pro's work at the cost of Flash. For Java developers, this means that you can access the quality of code close to the flagship at lower API prices, especially for long-range engineering tasks.
My proposal: The daily Java development is fully adequate for V4.1-Flash, with very high value for money. Only when dealing with ultra-complex architecture design will it be necessary to cut to Opus 5.5 or V4-Pro.
6. Qwen 3.8-Max: Invisible champion of terminal operations
Flagship of Ali Qwen. Its Terminal-Bench 2.1 gets 86.6, QwenSWE 80.7, LiveCodeBench v.6 92.6. Three scores are on the edge of the first squadron.
Qwen3.8-Max is characterized by strong end operation and engineering capabilities. Qwen's official blog shows its case of a self-enactment tool chain built for more than 10 days, a long-range implementation capability that is valuable in the construction, deployment and production of CI/CD scripts for Java.
Its Java ecological understanding is also in place, and the processing of the details of Maven/Gradle dependency management, Spring Boot automatic configuration, and JVM parameter optimization is more mature.
V. Third Platoon: each of the most skilled students Sub.
The model for this tranche has bright spots at a particular dimension, but is less integrated than the first two. Winning is unique and appropriate for a given scene.
7. Space-Bunny (Mini Max M3.1 Flash): DeepSWE ' s Black Horse
Mini Max, M3.1 Flash of publish on 19 October 2026, officially confirmed as the early version of OpenRouter's "Space Bunny". 428B MoE structure, very low price ($0.15/M input token).
Its DeepSWE v1.1 received 73.7 per cent, ranked fourth in total, after V4.1-Flash, Opus 5.5, V4-Pro (74.2 third). The 23.0 on SWE Refore Bench was more than twice the DeepSeek score with the benchmark.
Space-Bunny's character is "cheap and good." As the Flash model, it has performed more than many Pro-level models on the software engineering benchmark. A budget-sensitive team that requires engineering coding capacity is a highly competitive option.
Kimi K3: The power of an open source giant terminal
Open-source flagship on the dark side of the moon, 3T class MoE model for the total parameter 2779.9B. Its Terminal-Bench 2.1 received 88.3 after V4.1-Flash 90.6 and higher than Qwen 3.8-Max 86.6. DeepSWE 67.3%, Programme Bench 77.8.
The core advantage of Kimi K3 is open source + large parameter + long context (1M). Its performance in terminal operations and long-range missions is close to closing-source flagships and its weights are fully open and can be deployed locally. Kimi K3 is an alternative to V4-Pro for Java teams with data compliance requirements and strong coding capabilities.
It should be noted that the activation parameters of Kimi K3 are also large and that local deployments are extremely demanding for VRAM (about 1.6 TBVRAM is still required after quantization), and that ordinary developers have difficulty running, mainly to enterprises with a credit mass.
Step-5-Preview: Multilingual SE expert
The flagship of publish in September 2026, 600B MoE, with a "shrunk but deep" structure (92nd floor transformer block). Its StepCodeBench received 49.0. This benchmark covers 553 warehouses, 33 programming languages (which explicitly include Java), and Step-5 leads 40.2 GLM-5.3 and 43.9 Kimi K3. Programme Bench 805 is also close to 82.3 for Claude Opus 5.
Step-5 was designed to make deeper reasoning with fewer activation parameters. It performed well in the real multilingual software engineering landscape, in particular warehouse-level tasks involving static-type languages such as Java and Go.
10. Hy4 preview: Multilingual Java direct high score
Open-source flagship of publish, Tencent, August 2026, 770B MoE (49B activated). Its brightest is SWE-bench Multililingual 82.9 per cent, which is second only to the multilingual benchmark scores of the Java series, namely, Opus 5.5 and Fable 5.1, and above the 76.2 of V4-Pro.
Terminal-Bench 2.1 for Hy4 is 85.4, DeepSWE 64.3 per cent. Of the 163 experts in Tencent, 203 project assignments, Hy4 received 2.99/4.00, leading 2.92 GLM-5.3 and 2.94 Kimi K3.
For Java developers, the high score of the SWE Multilingual of Hy4 is a real signal that it is indeed competitive in the Java multilingual engineering mission. However, its pure algorithm benchmark (BenchLM coding point estimate 56.7) is relatively conservative, indicating that it is more engineering-oriented than algorithmic competition.
Six, four, five. Enough but not amazing.
11. Qwen3.7-Plus
As Qwen ' s non-flagged vessel, it is already good at LiveCodeBench v689.6 and SWE-bench Verified 77.7 per cent. The day-to-day Java CRDD development, interface preparation, simple bug repair was fully adequate and cheaper than Max. Suitable as a day-to-day master model, complex missions are then cut to Max or closed-source flagships.
12. GLM-5.3
The flagship of Zhipu has significantly increased its programming capacity (Z.ai Code Bench + 50 per cent) over GLM-5.2. DeepSWE 66.9%, StepCodeBench 40.2. However, in comparison with the new model in the second half of 2026, it has been reversed by Kimi K3, Step-5, Space-Bunny and others over a number of benchmarks. Winning is open-source and ecologically mature, as an option.
13. Seed-2.1-Turbo
Model of byte beat Doubao, LiveCodeBench v.6 66.1, SWE-bench Verified 56.8%. Its end-to-end Coding process is good in the developers ' measurements, but the pure reference score is indeed lower than the model above. The stability of Java's complex tasks also has room for improvement.
14. DeepSeek-V4-Flash
This is the first-generation Flash of the DeepSeek V4 series, with the LiveCodeBench new snapshot of August 2026 only 48.7 and clearly weaker than the V4-Pro and V4.1-Flash of the same series. A simple Java code segment generation is appropriate and not recommended for complex engineering tasks. If the budget is limited, it is recommended to go directly to V4.1-Flash, and experience varies considerably.
VII. Java-specific: which benchmarks really determine Java coding capacity
Many Java developers ask me, "Is it true that Python is so high in these benchmarks that it reflects Java's ability?" I'm going to take out the most relevant benchmarks for Java.
SWE-bench Multilingual is the strongest benchmark for Java. It added to Python the task of repairing the real warehouse in Java, JS, Go, etc. Java developers see this score as the most valuable reference point. 93.9 per cent of Opus 5.5, 89.1 per cent of Fable 5.1, 82.9 per cent of Hy4 and 76.2 per cent of V4-Pro, which is essentially the ranking of Java's multilingual engineering capability.
StepCodeBench is another hard benchmark with Java. It is built by StepFun and covers 33 programming languages, which explicitly include Java. Opus 5 runs in 63.9, GPT-6 Astra 61.0 closely followed by Step-5 49.0, Kimi K3 43.9, GLM-5.3 40.2. This benchmark focuses more on the multilingual tasks of the real warehouse and is of high reference value for the Java project.
While DeepSWE was mainly a Python warehouse, it measured long-range engineering capabilities. Long-range capability is common -- one that can do it in Python's warehouse. Change the code. The engineering methodology remained valid when the model of running tests to fix the full-process bug was moved to the Java warehouse. So the DeepSWE High Score Model (V4.1-Flash 77.0, Opus 5.5 74.2, Space-Bunny 73.7) will not be bad for Java's long-range mission.
There is also an easy point to ignore: the strong type of Java and the character of the compilation period check. The Python code is wrong and probably wrong when it's running, and Java code is wrong to compile. This means that for Java, the "one-time pass rate" for model generation codes is more critical than "logically correct". Opus 5.5 and Fable 5.1 perform better in this respect because their codes are more capable of authenticating themselves and are capable of self-inspecting, after generation, the issue of the matching of types, pan-boundary, annotated parameters, etc.
VIII. FIELD SPECIFIC: Who should use different Java scenarios
Theoretically, it's going to end up like, "Who should I use?" I'm suggesting a direct selection based on the typical scene of Java developers.
Scenario one: Reconstruct/multimodule migration of the large Spring Boot project. Opus 5.5. Its SWE Multilingual and SWE Pro were the highest on the scene and the most comprehensive understanding of the Java Project. Complex reliance on adjustments, configuration migration, interface compatibility processing, with the lowest error rate.
Scenario 2: Complex algorithms/data structure Java achieved. Choose Fable 5.1 or V4-Pro. LCB 90.5 of Fable 5.1 and the self-certification capability are suitable for the writing of algorithm codes that require precise boundary processing. V4-Pro LCB 93.5 (old snapshot) algorithms are also extremely powerful and open-source.
Scenario III: Daily CRUD development/interface preparation. V4.1-Flash or Qwen 3.7-Plus. V4.1-Flash has an invincible value for money, and SWE Verified 77.7 per cent of Qwen 3.7-Plus is also sufficient for day-to-day development. The experiences of the two models in the conventional Java development landscape and the flagship are not very different.
Scenario IV: Old Java system adaptation/database migration. Select GPT-6 Astra. Its database migration benchmark was the highest 57.9 per cent, and FrontierSWE and StepCodeBench were strong. Addressing the task of "reading the old code transferred to the new framework six years ago" is the advantage of Astra's long-range understanding.
Scenario 5: Data compliance requirements/must be locally deployed. Select V4-Pro or Kimi K3. V4-Pro is more integrated and the Kimi K3 terminal operates more intensively, but deployment costs are higher (a large amount of VRAM is required). V4-Pro is a more realistic option if the ability to calculate is limited.
Scenario 6: budget sensitive but engineering capacity required. Choose Space-Bunny or V4.1-Flash. The prices of DeepSWE 73.7 per cent and 0.15/M for Space-Bunny were virtually non-opposed. The combined capacity of V4.1-Flash is slightly stronger and prices are low. These two are the first choice for value for money.
Rating and final ranking
Based on all the above-mentioned baseline data, the five dimensions of the Java code (the algorithm achieves 30 per cent, Java language and ecology 20 per cent, software engineering 25 per cent, debugging 15 per cent, code quality 10 per cent) are given a comprehensive score (full 100):
X. A few facts that can easily be ignored
In writing this article, there are several points that I feel I have to say alone, because many people step on the pit when they choose models.
First, don't look at only one score. Some models are very high on the LCB, but the SWE Multilingual is general, indicating that it has excellent algorithms but general engineering capability. Java has been working on the project code for 90 per cent of its time, not on algorithms, so these project benchmarks, SWE Multilingual, DeepSWE, StepCodeBench, are more important to you.
Secondly, snapshots and versions are more important than scores. The same model can get 93 in the old LCB snapshot, and the new snapshot is only 52, not the model is getting weaker, it's getting difficult. Terminal-Bench 2.1 and 4.0. It is important to look at which version of the benchmark.
Thirdly, the manufacturer ' s self-rated scores are discounted. Many high scores come from manufacturers under their own harness, optimal configuration, and independent third-party re-examination tends to be lower. For example, the BenchLM coding point at Hy4 is estimated at 56.7 and there is a gap with the manufacturer's data. For reference, priority is given to data from independent third parties (Officials BenchLM, Continuum, LiveCodeBench).
Fourth, Java's compilation period features make slow models more advantageous. Fable 5.1, Opus 5.5 models that take time to validate themselves have a greater advantage in Java than in Python. Since Java is nothing but zero, it is worthwhile to spend more on the type of token check and note.
Fifth, gaps in open source models are rapidly closing. A year ago the coding gap between open and closed sources was significant, and now V4.1-Flash's DeepSWE has surpassed Opus 5.5. V4-Pro and Kimi K3 are also close to closing source flagships on multiple benchmarks. If trends continue, it is only a matter of time before the open source model closes the source on Java code.
11, the end, not ending
Models are too late, and the scores in this article may be out of date in three months. However, the methodology will not be out of date: the Java capability looks at SWE Multilingual and StepCodeBench, the engineering capability at DeepSWE and SWE Pro, and the algorithm at LCB. You'll get through these benchmarks, and you'll be able to quickly locate the level of any new model.
For Java developers, the most practical strategy at this time is to develop high-value models such as V4.1-Flash or Space-Bunny on a daily basis, which are subject to complex restructuring or migration of older systems to Opus 5.5 or GPT-6 Astra. Open-source compliance requirements are above V4-Pro. You don't have to go for the strongest. You go for the best. After all, the model is just a tool, a running, maintenance, extended Java code, and ultimately your understanding of business and technology. Models help you speed up, but they can't think for you.
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