AI Programming · AIGC · OpenAI

14 LLMs Benchmarked for Java Coding: Closed-Source Trio Leads, Open Source Narrows the Gap

By 殷紫川 · Oct 23, 2026

Read original on juejin.cn ↗ Google Translate ↗ Alt translation

Java developers now have a benchmark map that separates algorithm skill from repository-level engineering, with SWE-bench Multilingual and StepCodeBench as the signals that include real Java tasks. The open-source gap is shrinking fast enough that budget Flash models beat premium closed flagships on one long-horizon benchmark, so cost, not capability, decides the daily driver.

Summary

Across LiveCodeBench, SWE-bench Multilingual, DeepSWE, Terminal-Bench, and Java-specific tests, 14 models sort into clear tiers. Claude Opus 5.5 leads repository-level software engineering with 93.9% on SWE-bench Multilingual and 89.9% on SWE-bench Pro, making it the safest choice for big Spring Boot refactors. Claude Fable 5.1 pairs a 90.5 LiveCodeBench score with self-verification that delivers 73.4% on CursorBench. GPT-6 Astra dominates agentic work: ProgramBench 85.4, StepCodeBench 61.0, FrontierSWE v2 65.5%, and 57.9% on a database migration benchmark.

Open-source models sit one step behind but close fast. DeepSeek-V4-Pro is the open-weight leader with SWE-bench Verified 80.6% and SWE Multilingual 76.2%, and V4.1-Flash posts the highest DeepSWE v1.1 score in the entire lineup at 77.0% while priced as a Flash model. Qwen3.8-Max and Space-Bunny each threaten the top tier on terminal and engineering benchmarks, and Kimi K3's open 3T-parameter design brings near-flagship terminal scores at the cost of enormous VRAM needs.

A weighted Java score in the comparison ranks Opus 5.5 first at 93, Fable 5.1 second at 90, GPT-6 Astra third at 89, and DeepSeek-V4-Pro fourth at 88. Daily Java work can safely run on V4.1-Flash or Space-Bunny, while complex refactors and legacy migrations call for Opus 5.5 or GPT-6 Astra, and compliance-bound teams self-host V4-Pro.

Takeaways

— Claude Opus 5.5 posts the best repository-level software engineering scores: 93.9% on SWE-bench Multilingual and 89.9% on SWE-bench Pro, making it the recommended model for large Spring Boot refactors.

— Claude Fable 5.1 uses self-verification to reach 73.4% on CursorBench and 89.1% on SWE Multilingual; it excels at complex concurrency and JVM-level Java but trades speed and token cost.

— GPT-6 Astra leads agentic and long-horizon coding with ProgramBench 85.4, StepCodeBench 61.0, FrontierSWE v2 65.5%, and a 57.9% score on OpenAI's internal database migration benchmark.

— DeepSeek-V4-Pro is the strongest open-weight option: SWE-bench Verified 80.6%, SWE Multilingual 76.2%, and LiveCodeBench 93.5 on the old snapshot, with local deployment for code that cannot leave the company.

— DeepSeek-V4.1-Flash scores 77.0% on DeepSWE v1.1, the highest in the 14-model lineup, beating Opus 5.5's 74.2% at Flash-tier speed and price.

— Qwen3.8-Max reaches Terminal-Bench 2.1 86.6, QwenSWE 80.7, and LiveCodeBench v6 92.6, with an official demonstration of more than 10 days of autonomous coding.

— MiniMax's Space-Bunny, identified as the early version of M3.1 Flash, scores 73.7% on DeepSWE v1.1 and costs $0.15 per million input tokens.

— Kimi K3, an open 2779.9B-parameter MoE with 1M context, scores 88.3 on Terminal-Bench 2.1 but needs roughly 1.6TB VRAM after quantization.

— Step-5-Preview leads StepCodeBench among open challengers at 49.0, on a benchmark spanning 553 repositories and 33 languages including Java.

— Hy4 preview scores 82.9% on SWE-bench Multilingual, second only to the two Claude models, though its independent BenchLM coding estimate is just 56.7.

— Terminal-Bench 2.1 and 4.0 are different difficulties, and LiveCodeBench snapshots vary heavily; cross-version scores are not directly comparable.

— The weighted Java ranking puts Opus 5.5 at 93, Fable 5.1 at 90, GPT-6 Astra at 89, DeepSeek-V4-Pro at 88, and V4.1-Flash at 87.

Conclusions

Benchmark versions are the hidden variable: a model can score 93.5 on one LiveCodeBench snapshot and 52.8 on another, so any comparison that omits the snapshot is worthless.

Flash-tier economics have shifted: DeepSeek-V4.1-Flash and Space-Bunny now deliver flagship-level long-horizon engineering results at low cost, which makes premium models harder to justify for routine Java work.

Algorithm benchmarks mislead Java developers: the same model can look brilliant on LiveCodeBench yet average on repository engineering, and Java work is mostly engineering, not contest coding.

Vendor-reported scores deserve skepticism; Hy4's official multilingual strength coexists with a 56.7 BenchLM coding estimate, so independent benchmarks should anchor purchasing decisions.

Java's compile-time checks turn self-verifying models into a bigger advantage than in Python, since a model that catches type and generics errors before compilation saves a full feedback loop.

Concepts & terms

LiveCodeBench (LCB)

A continuously updated coding benchmark built from real competitive programming problems; different snapshot versions vary in difficulty and cannot be compared directly.

SWE-bench Multilingual

A repository-level software engineering benchmark that adds Java, JavaScript, Go, and other languages to real GitHub issue-fixing tasks; the strongest signal for Java coding ability.

SWE-bench Verified

A strictly validated subset of SWE-bench where a model fixes real GitHub issues and must pass hidden tests.

DeepSWE v1.1

A benchmark for long-horizon software engineering where the model autonomously reads, edits, runs, and debugs code in a terminal environment.

Terminal-Bench

A benchmark for terminal and agentic operations; versions 2.1 and 4.0 differ in difficulty and should not be compared directly.

StepCodeBench

A multilingual software engineering benchmark built by StepFun covering 553 repositories and 33 languages, including Java.

MoE (Mixture-of-Experts)

A model architecture with a large total parameter count but only a subset of parameters activated for each token, balancing scale and compute cost.

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