14 Major LLMs Benchmarked for Java Coding Ability: The Closed-Source Top Three Lead by a Wide Margin, How Far Has Open Source Caught Up?

As an engineer who has written Java for a long time and also tracks LLM advances, I deal with all kinds of coding models every day. Model iteration is too fast: a benchmark score that sits at the top of the leaderboard today can be refreshed next month. This post systematically breaks down the Java coding ability of 14 current mainstream models. All data comes from official vendor releases and third-party independent evaluations (BenchLM, Continuum, LiveCodeBench, DeepSWE, etc.). No filler, no made-up numbers.

1. First, clarify: how is coding ability actually measured

When discussing model coding ability, you cannot look at a single score. Different benchmarks test completely different things, and mixing them together is meaningless. The industry-recognized coding ability evaluations currently take four main forms:

Category 1: Algorithms and code generation. Represented by LiveCodeBench (LCB). Problems come from real competition platforms and are continuously updated to prevent data contamination. This benchmark reflects a model's ability to solve algorithmic problems and write function-level code. Note: different LCB snapshots vary greatly in difficulty. A first-half-2026 snapshot can reach 90+, while a new snapshot from the second half generally drops to around 50. Scores across snapshots cannot be compared directly.

Category 2: Repository-level software engineering. Represented by SWE-bench Verified and SWE-bench Pro. A model is given a real GitHub repository issue and must locate code, modify it, and pass tests. This benchmark is closest to daily development, but also the hardest. SWE-bench Verified is a strictly validated subset; SWE-bench Pro is a harder professional version.

Category 3: Multilingual software engineering. Represented by SWE-bench Multilingual, which adds real repository tasks in Java, JavaScript, Go, and other languages besides Python. This benchmark has the highest reference value for Java developers, because it directly includes Java engineering tasks.

Category 4: Agentic long-horizon tasks. Represented by DeepSWE v1.1, Terminal-Bench, StepCodeBench, and ProgramBench. These benchmarks require a model to autonomously complete multi-step operations in a terminal environment: write code, run commands, read errors, fix bugs, and verify again. DeepSWE tests long-horizon software engineering, Terminal-Bench tests terminal operation ability, StepCodeBench covers 553 repositories and 33 languages (including Java), and ProgramBench tests the full daily programming workflow.

There are two pitfalls to keep in mind: Terminal-Bench 2.1 and 4.0 are not the same difficulty; 4.0 is harder, so V4.1-Flash's 90.6 (2.1) and Opus 5.5's 66.4 (4.0) cannot be compared as-is. The same applies to LiveCodeBench snapshots, where 93.5 and 54.2 come from question banks at different points in time. All rankings below are horizontal comparisons within the same benchmark, not crude additions across versions.

2. Core data at a glance

Here is the full comparison table, laying out the key scores of the 14 models. Empty cells mean that model has not publicly released a score for that benchmark.

Model	LCB	SWE Pro	SWE Multilingual	DeepSWE v1.1	Terminal-Bench	Java/SE specific
Claude Opus 5.5	—	89.9	93.9	74.2	66.4 (4.0)	FrontierSWE 62.3
Claude Fable 5.1	90.5	81.2	89.1	67.4	55.8 (4.0)	CursorBench 73.4
GPT-6 Astra	—	—	—	—	57.7 (4.0)	ProgramBench 85.4, StepCodeBench 61.0
DeepSeek-V4-Pro	93.5 (old)/52.8 (Aug)	55.4	76.2	62.7	—	SWE-Verified 80.6
DeepSeek-V4.1-Flash	54.2 (Aug)	—	—	77.0	90.6 (2.1)	—
Qwen3.8-Max	92.6 (v6)	—	—	—	86.6 (2.1)	QwenSWE 80.7
Space-Bunny	—	—	—	73.7	—	SWE Refore 23.0
Kimi K3	—	—	—	67.3	88.3 (2.1)	ProgramBench 77.8
Step-5-Preview	—	—	—	—	85.0 (2.1)	StepCodeBench 49.0
Hy4 preview	—	—	82.9	64.3	85.4 (2.1)	Internal blind eval 2.99/4
Qwen3.7-Plus	89.6 (v6)	—	—	—	—	SWE-Verified 77.7
GLM-5.3	—	—	—	66.9	28.3 (3.0)	StepCodeBench 40.2
Seed-2.1-Turbo	66.1 (v6)	—	—	—	—	SWE-Verified 56.8
DeepSeek-V4-Flash	48.7 (Aug)	—	—	—	—	—

Data sources: Anthropic system cards, OpenAI release pages, DeepSeek official changelogs, Qwen official blog, Kimi technology blog, MiniMax blog, StepFun official page, Tencent Hunyuan release page, Zhipu AI, BenchLM, Continuum, and the official LiveCodeBench leaderboard.

3. Tier 1: closed-source top three, leading by a wide margin

These three models are the current ceiling for coding ability. They take the top three spots across multiple core benchmarks and have opened an obvious gap behind them.

1. Claude Opus 5.5: the all-around king

Anthropic's new flagship released in October 2026, positioned as “Fable-level capability at lower cost.” It posts 93.9% on SWE-bench Multilingual, the highest of all models, and that benchmark directly includes Java repository tasks, so for Java developers this is hard currency. SWE-bench Pro 89.9% is also first overall, and DeepSWE 74.2% ranks third.

Opus 5.5's strength is balance. No single metric explodes, but the whole software engineering pipeline is strong: understanding requirements, locating code, modifying implementation, running tests, and verifying; it does not drop the ball at any step. In Anthropic's official data, its GDPval-AA v2.1 reaches 1846 Elo, ahead of Fable 5.1's 1735, indicating higher completion on real professional tasks.

For Java developers, the practical meaning: if you are working on large Spring Boot refactors, multi-module dependency adjustments, or complex business logic migration, Opus 5.5 is currently the most stable choice. It understands error-prone points such as Java's strong typing constraints, annotation configuration, and generics boundaries, and the code it generates has a higher compile pass rate and test pass rate.

2. Claude Fable 5.1: the perfectionist of reasoning and self-verification

Fable 5.1 is Anthropic's reasoning flagship, released in September 2026. Its most prominent ability is verifying its own code. Anthropic officially says it scored 73.4% on CursorBench 3.2, the highest in Anthropic history, because it repeatedly checks whether the code it wrote can actually run.

Key scores: SWE Multilingual 89.1% (second overall), LiveCodeBench 90.5, SWE Pro 81.2%, DeepSWE 67.4%.

The Fable line's design philosophy is “slow work yields fine products.” It spends more tokens to think and verify, so it stands out on difficult algorithm problems and engineering tasks that require repeated debugging. The cost is slower speed and higher cost.

From my actual use: when I use Fable 5.1 to write complex Java concurrency code, JVM tuning scripts, or utility classes that require precise understanding of generic erasure, its error rate is noticeably lower than other models. But if you just write a simple Controller, its thinking process can seem wordy.

3. GPT-6 Astra: the new king of agentic programming

OpenAI's new flagship, which the company directly calls “the best model for software engineering to date.” Its agentic programming benchmark results are striking: ProgramBench 85.4 (highest overall, above Claude Opus 5's 82.3 and Step-5's 80.5), StepCodeBench 61.0 (a multilingual benchmark including Java, second overall, behind only Opus 5's 63.9), and FrontierSWE v2 65.5% (overtaking Opus 5.5's 62.3%).

Astra's strengths are long-horizon tasks and codebase understanding. On OpenAI's internal database migration benchmark, Astra scores 57.9%, ahead of Fable 5.1's 55.8% and GPT-5.6 Sol's 37.3%. That suggests strong ability in the typical old-Java-system migration workflow of “read old code → understand logic → migrate to a new framework → verify.”

Its weakness: it has published relatively few traditional benchmark scores such as SWE Multilingual and SWE Verified, so comparisons with Opus 5.5 and Fable 5.1 rely mainly on agentic benchmarks. But from available data, Astra is extremely competitive in real engineering scenarios.

4. Tier 2: open-source and domestic flagships, approaching but not surpassing

Models in this tier can challenge Tier 1 on certain single benchmarks, but their overall software engineering ability still lags. The good news: they are open source or price-friendly, so their cost-performance stands out.

4. DeepSeek-V4-Pro: the benchmark for open-source coding

DeepSeek-V4-Pro is the ceiling of coding ability among open-source models. LiveCodeBench 93.5 (old snapshot), SWE-bench Verified 80.6%, and SWE Multilingual 76.2%: put together, no open-source model beats it.

Its DeepSWE score is 62.7%, which looks lower than some later models, but note that DeepSWE only became popular in the second half of 2026, and V4-Pro was released earlier with an early configuration. After V4.1-Flash was released, it scored 77.0% on the same DeepSWE, showing DeepSeek's post-training optimization still has a lot of headroom.

V4-Pro's advantage is comprehensive ability combined with open, deployable weights. For companies that cannot send code to closed APIs, V4-Pro is currently the best option for a self-hosted coding model. On Java, its code generation quality for mainstream frameworks such as Spring, MyBatis, and Dubbo is high, because Chinese Java projects make up a large share of its training corpus.

5. DeepSeek-V4.1-Flash: a Flash model that outdoes flagships

Released in September 2026, this is the most surprising model. As a Flash version (fast, low cost), it scores 77.0% on DeepSWE v1.1, the highest overall, above Opus 5.5's 74.2% and Space-Bunny's 73.7%. On the August 2026 new LiveCodeBench snapshot, it scores 54.2, also above V4-Pro's 52.8.

V4.1-Flash uses a new asymmetric codec architecture and larger-scale reinforcement learning post-training. Its positioning is clear: deliver Pro-level work at Flash cost. For Java developers, this means you can get near-flagship coding quality at a lower API price, especially for long-horizon engineering tasks.

My suggestion: for daily Java development, V4.1-Flash is fully sufficient and offers excellent value. Only for ultra-complex architecture design do you need to switch to Opus 5.5 or V4-Pro.

6. Qwen3.8-Max: the stealth champion of terminal operations

The flagship of Alibaba's Tongyi Qianwen. It scores 86.6 on Terminal-Bench 2.1, 80.7 on QwenSWE, and 92.6 on LiveCodeBench v6. All three sit near the edge of Tier 1.

Qwen3.8-Max is characterized by strong terminal operation and engineering ability. The Qwen official blog showed it autonomously coding for more than 10 consecutive days and building a self-evolving toolchain. That kind of long-horizon execution is valuable for Java build, deployment, and CI/CD script generation.

Its understanding of the Java ecosystem is also solid, with mature handling of engineering details such as Maven/Gradle dependency management, Spring Boot auto-configuration, and JVM parameter tuning.

5. Tier 3: middle-generation models with individual specialties

Models in this tier shine in one particular dimension, but overall ability is below the first two tiers. Their strengths are distinctive and suit specific scenarios.

7. Space-Bunny (MiniMax M3.1 Flash): the DeepSWE dark horse

MiniMax released M3.1 Flash on October 19, 2026, and officially confirmed that the anonymously leading “Space Bunny” on OpenRouter was an early version. 428B MoE architecture, extremely low price ($0.15/M input tokens).

Its DeepSWE v1.1 score is 73.7%, fourth overall, behind V4.1-Flash, Opus 5.5, and V4-Pro (74.2 counts as third). On SWE Refore Bench (code repair specialization) it scores 23.0, more than twice DeepSeek's score on the same benchmark.

Space-Bunny's characteristic is “cheap and capable.” As a Flash model, its software engineering benchmark performance already beats many Pro-level models. For teams that are budget-sensitive but need engineering coding ability, it is a very cost-effective choice.

8. Kimi K3: open-source behemoth with terminal dominance

Moonshot AI's open-source flagship, a 3T-level MoE model with 2779.9B total parameters. Terminal-Bench 2.1: 88.3, second only to V4.1-Flash's 90.6 and above Qwen3.8-Max's 86.6. DeepSWE 67.3%, ProgramBench 77.8.

Kimi K3's core advantages are open source, huge parameters, and a long 1M context. Its terminal operation and long-horizon task performance approach closed-source flagships, and the weights are fully open for local deployment. For Java teams with data compliance requirements that still need strong coding ability, Kimi K3 is an alternative to V4-Pro.

Note that Kimi K3's activated parameters are also very large, and local deployment demands extreme GPU memory (still around 1.6TB VRAM after quantization), so ordinary developers can hardly run it. It mainly targets enterprises with compute clusters.

9. Step-5-Preview: specialist in multilingual software engineering

StepFun's flagship released in September 2026, 600B MoE, with a “narrow but deep” architecture (92 transformer blocks). StepCodeBench: 49.0. That benchmark covers 553 repositories and 33 programming languages (including Java explicitly), and Step-5 leads GLM-5.3's 40.2 and Kimi K3's 43.9 on it. ProgramBench 80.5 is also close to Claude Opus 5's 82.3.

Step-5's design idea is to use fewer activated parameters for deeper reasoning. Its performance in real multilingual software engineering scenarios is solid, especially on repository-level tasks involving statically typed languages like Java and Go.

10. Hy4 preview: a direct high score on multilingual Java

Tencent Hunyuan's open-source flagship released in August 2026, 770B MoE with 49B activated. Its most eye-catching score is SWE-bench Multilingual 82.9%. Among multilingual benchmarks that include Java, that is second only to Opus 5.5 and Fable 5.1, and above V4-Pro's 76.2%.

Hy4's Terminal-Bench 2.1 is 85.4, and DeepSWE is 64.3%. In Tencent's internal blind evaluation with 163 experts and 203 engineering tasks, Hy4 scored 2.99/4.00, ahead of GLM-5.3's 2.92 and Kimi K3's 2.94.

For Java developers, Hy4's high SWE Multilingual score is a practical signal that it is genuinely competitive on Java multilingual engineering tasks. Its pure algorithm benchmark (BenchLM coding point estimate 56.7) is more conservative, indicating it leans engineering rather than algorithm competition.

6. Tiers 4 and 5: adequate but not impressive

11. Qwen3.7-Plus

As the non-flagship tier of Qwen, its LiveCodeBench v6 89.6 and SWE-bench Verified 77.7% are already quite good. It is fully sufficient for daily Java CRUD development, API writing, and simple bug fixes, and its price is much lower than Max. It suits being the main model for daily development, with a switch to Max or a closed-source flagship for complex tasks.

12. GLM-5.3

Zhipu's flagship. Coding ability has improved significantly over GLM-5.2 (Z.ai Code Bench +50%). DeepSWE 66.9%, StepCodeBench 40.2. But compared with new models from the second half of 2026, it has already been overtaken on multiple benchmarks by Kimi K3, Step-5, Space-Bunny, and others. Its advantages are open source and a mature ecosystem; it works as a backup choice.

13. Seed-2.1-Turbo

ByteDance Doubao's model. LiveCodeBench v6 66.1, SWE-bench Verified 56.8%. Its end-to-end coding flow has a good reputation in developer mass testing, but its raw benchmark scores are lower than the models above. Stability on complex Java tasks still has room to improve.

14. DeepSeek-V4-Flash

The first Flash in the DeepSeek V4 series. On the August 2026 new LiveCodeBench snapshot it scores only 48.7, clearly weaker than V4-Pro and V4.1-Flash from the same family. It suits simple Java code snippet generation, not complex engineering tasks. If the budget is limited, go straight to V4.1-Flash; the experience gap is large.

7. Java-specific: which benchmarks really determine Java coding ability

Many Java developers ask me: “Python makes up such a large share of these benchmarks, can they reflect Java ability?” That question is fair. I will pull out the benchmarks most relevant to Java.

SWE-bench Multilingual is the benchmark with the strongest Java signal. In addition to Python, it adds real repository issue-fixing tasks in Java, JS, Go, and other languages. Java developers should use this score as the most useful reference. Opus 5.5's 93.9%, Fable 5.1's 89.1%, Hy4's 82.9%, and V4-Pro's 76.2% basically form the ranking for multilingual Java engineering ability.

StepCodeBench is another hard benchmark that includes Java. Built by StepFun, it covers 33 programming languages and explicitly includes Java. Opus 5 leads at 63.9, GPT-6 Astra follows at 61.0, Step-5 is 49.0, Kimi K3 is 43.9, and GLM-5.3 is 40.2. This benchmark emphasizes real-repository multilingual tasks, so its reference value for Java projects is high.

DeepSWE is mainly Python repositories, but it measures long-horizon engineering ability. Long-horizon ability is universal. A model that can autonomously complete the full “understand requirements → change code → run tests → fix bugs” loop in a Python repository will still apply that engineering methodology when transferred to a Java repository. So high-scoring DeepSWE models (V4.1-Flash 77.0, Opus 5.5 74.2, Space-Bunny 73.7) should also do well on long-horizon Java tasks.

There is another point that is easy to overlook: Java's strong typing and compile-time checking. Python code can fail only at runtime when written incorrectly; Java code fails at compilation when written incorrectly. For Java, this means a model's “first-pass compile success rate” matters more than “logical correctness rate.” Opus 5.5 and Fable 5.1 perform better here because their code self-verification is stronger; they can check type matching, generics boundaries, annotation parameters, and other compile-time issues after generation.

8. Practical model selection: who to use for which Java scenario

No matter how much theory is said, the final question is “which one should I actually use.” Here is direct selection advice based on typical Java developer scenarios.

Scenario 1: Large Spring Boot project refactoring / multi-module migration. Choose Opus 5.5. Its SWE Multilingual and SWE Pro are both the highest overall, and its understanding of Java engineering is the most comprehensive. For complex dependency adjustments, configuration migration, and interface compatibility handling, its error rate is the lowest.

Scenario 2: Complex algorithm / data structure implementation in Java. Choose Fable 5.1 or V4-Pro. Fable 5.1's LCB 90.5 and self-verification suit algorithm code requiring precise boundary handling. V4-Pro's LCB 93.5 (old snapshot) is also extremely strong on algorithms, and it is open source.

Scenario 3: Daily CRUD development / API writing. Choose V4.1-Flash or Qwen3.7-Plus. V4.1-Flash has unbeatable cost performance, and Qwen3.7-Plus's SWE Verified 77.7% is enough for everyday development. In routine Java development scenarios, these two feel close to flagships.

Scenario 4: Legacy Java system migration / database migration. Choose GPT-6 Astra. Its database migration benchmark of 57.9% is the highest overall, and FrontierSWE and StepCodeBench are also strong. For tasks like “understand six-year-old legacy code → migrate to a new framework,” Astra's long-horizon understanding is an advantage.

Scenario 5: Data compliance requirements / must deploy locally. Choose V4-Pro or Kimi K3. V4-Pro is stronger overall; Kimi K3 is stronger on terminal operations but has higher deployment cost (requires large GPU memory). If compute is limited, V4-Pro is the more realistic choice.

Scenario 6: Budget-sensitive but needs engineering ability. Choose Space-Bunny or V4.1-Flash. Space-Bunny's DeepSWE 73.7% at $0.15/M has almost no rival. V4.1-Flash has slightly stronger overall ability and also a low price. These two are the top cost-performance choices.

9. Scoring and final ranking

Based on all benchmark data above, combined with five dimensions of Java coding (algorithm implementation 30%, Java language and ecosystem 20%, software engineering 25%, debugging and fixing 15%, code quality 10%), the composite score (out of 100) is:

Rank	Model	Total score	One-line positioning
1	Claude Opus 5.5	93	The most stable all-rounder for Java across all scenarios
2	Claude Fable 5.1	90	Reasoning flagship with strong algorithm and self-verification
3	GPT-6 Astra	89	New king of agentic long-horizon engineering
4	DeepSeek-V4-Pro	88	Open-source coding benchmark, self-hostable
5	DeepSeek-V4.1-Flash	87	Flash price, flagship-level work: value king
6	Qwen3.8-Max	85	Outstanding terminal operation and engineering ability
7	Space-Bunny	83	DeepSWE dark horse, extreme cost performance
8	Kimi K3	82	Open-source 3T behemoth, terminal dominance
9	Step-5-Preview	81	Multilingual software engineering specialist
10	Hy4 preview	80	Direct high score on multilingual Java
11	Qwen3.7-Plus	79	Good enough for daily development, value choice
12	GLM-5.3	76	Clearly improved but overtaken by newer models
13	Seed-2.1-Turbo	73	Good end-to-end experience but low benchmark scores
14	DeepSeek-V4-Flash	68	Only suitable for simple code snippets

10. Several truths that are easy to overlook

While writing this, I think a few points must be called out separately, because many people will step on these traps when choosing models.

First, do not look at only one score. Some models score high on LCB but average on SWE Multilingual, meaning they are strong at algorithm problems but weak at engineering. Java developers spend 90% of their time writing engineering code, not algorithm problems, so engineering benchmarks such as SWE Multilingual, DeepSWE, and StepCodeBench matter more to you.

Second, snapshots and versions matter more than the score. The same model can score 93 on an old LCB snapshot and 52 on a new one. That is not the model getting weaker; the question bank got harder. The same applies to Terminal-Bench 2.1 and 4.0. When comparing, always check which benchmark version was used.

Third, discount vendor-reported scores. Many high scores are obtained by vendors using their own harness and optimal configuration; third-party independent retests are often lower. For example, Hy4's BenchLM coding point estimate is only 56.7, with a gap from vendor data. When referencing scores, prefer independent third-party data (BenchLM, Continuum, official LiveCodeBench).

Fourth, Java's compile-time characteristics give “slow models” an advantage. Models like Fable 5.1 and Opus 5.5 that spend time self-verifying have a bigger advantage on Java than on Python. If Java code does not compile, it is zero. Spending extra tokens to check types and annotations is worth it.

Fifth, the open-source gap is closing fast. A year ago the coding gap between open source and closed source was still large. Now V4.1-Flash's DeepSWE has already surpassed Opus 5.5. V4-Pro and Kimi K3 are also approaching closed-source flagships on multiple benchmarks. If the trend continues, open-source models catching up to closed source on Java coding is only a matter of time.

11. The end, not ending

Models iterate too fast; the scores in this post may be outdated in three months. But the evaluation methodology will not be outdated: for Java ability, watch SWE Multilingual and StepCodeBench; for engineering ability, watch DeepSWE and SWE Pro; for algorithms, watch LCB. Once you understand these benchmarks, you can quickly position any new model.

For Java developers, the most pragmatic strategy right now is: use high-cost-performance models like V4.1-Flash or Space-Bunny for daily development, switch to Opus 5.5 or GPT-6 Astra for complex refactoring or legacy system migration, and use V4-Pro for open-source compliance needs. Do not chase “the strongest”; chase “the most suitable.” After all, models are just tools. Writing Java code that runs, is maintainable, and can be extended ultimately depends on your understanding of the business and the technology. Models can speed you up, but they cannot think for you.