SWE-bench Multilingual

A multilingual benchmark for issue resolving in software engineering that covers Java, TypeScript, JavaScript, Go, Rust, C, and C++. Contains 1,632 high-quality instances carefully annotated from 2,456 candidates by 68 expert annotators, designed to evaluate Large Language Models across diverse software ecosystems beyond Python.

Claude Mythos Preview from Anthropic currently leads the SWE-bench Multilingual leaderboard with a score of 0.873 across 27 evaluated AI models.

Paper

AnthropicClaude Mythos Preview leads with 87.3%, followed by AnthropicClaude Opus 4.6 at 77.8% and Moonshot AIKimi K2.6 at 76.7%.

Progress Over Time

Interactive timeline showing model performance evolution on SWE-bench Multilingual

State-of-the-art frontier
Open
Proprietary

SWE-bench Multilingual Leaderboard

27 models
ContextCostLicense
1
21.0M$5.00 / $25.00
3
Moonshot AI
Moonshot AI
1.0T262K$0.95 / $4.00
4205K$0.30 / $1.20
51.6T1.0M$1.74 / $3.48
6
Alibaba Cloud / Qwen Team
Alibaba Cloud / Qwen Team
1.0M$0.50 / $3.00
7284B1.0M$0.14 / $0.28
8
Moonshot AI
Moonshot AI
1.0T
9230B1.0M$0.30 / $1.20
10309B
101.0T
12
Alibaba Cloud / Qwen Team
Alibaba Cloud / Qwen Team
28B262K$0.60 / $3.60
13685B164K$0.26 / $0.38
13685B
15
Alibaba Cloud / Qwen Team
Alibaba Cloud / Qwen Team
397B262K$0.60 / $3.60
16
Alibaba Cloud / Qwen Team
Alibaba Cloud / Qwen Team
35B
17
Zhipu AI
Zhipu AI
358B205K$0.60 / $2.20
181.0T
19685B
20
MiniMax
MiniMax
230B1.0M$0.30 / $1.20
21
Alibaba Cloud / Qwen Team
Alibaba Cloud / Qwen Team
480B
22671B
231.0T
23
Moonshot AI
Moonshot AI
1.0T
25120B
2669B256K$0.10 / $0.40
27671B131K$0.55 / $2.19
Notice missing or incorrect data?

FAQ

Common questions about SWE-bench Multilingual.

What is the SWE-bench Multilingual benchmark?

A multilingual benchmark for issue resolving in software engineering that covers Java, TypeScript, JavaScript, Go, Rust, C, and C++. Contains 1,632 high-quality instances carefully annotated from 2,456 candidates by 68 expert annotators, designed to evaluate Large Language Models across diverse software ecosystems beyond Python.

What is the SWE-bench Multilingual leaderboard?

The SWE-bench Multilingual leaderboard ranks 27 AI models based on their performance on this benchmark. Currently, Claude Mythos Preview by Anthropic leads with a score of 0.873. The average score across all models is 0.644.

What is the highest SWE-bench Multilingual score?

The highest SWE-bench Multilingual score is 0.873, achieved by Claude Mythos Preview from Anthropic.

How many models are evaluated on SWE-bench Multilingual?

27 models have been evaluated on the SWE-bench Multilingual benchmark, with 0 verified results and 27 self-reported results.

Where can I find the SWE-bench Multilingual paper?

The SWE-bench Multilingual paper is available at https://arxiv.org/abs/2504.02605. The paper details the methodology, dataset construction, and evaluation criteria.

What categories does SWE-bench Multilingual cover?

SWE-bench Multilingual is categorized under reasoning and code. The benchmark evaluates text models with multilingual support.

More evaluations to explore

Related benchmarks in the same category

View all reasoning
GPQA

A challenging dataset of 448 multiple-choice questions written by domain experts in biology, physics, and chemistry. Questions are Google-proof and extremely difficult, with PhD experts reaching 65% accuracy.

reasoning
214 models
MMLU-Pro

A more robust and challenging multi-task language understanding benchmark that extends MMLU by expanding multiple-choice options from 4 to 10, eliminating trivial questions, and focusing on reasoning-intensive tasks. Features over 12,000 curated questions across 14 domains and causes a 16-33% accuracy drop compared to original MMLU.

reasoning
119 models
AIME 2025

All 30 problems from the 2025 American Invitational Mathematics Examination (AIME I and AIME II), testing olympiad-level mathematical reasoning with integer answers from 000-999. Used as an AI benchmark to evaluate large language models' ability to solve complex mathematical problems requiring multi-step logical deductions and structured symbolic reasoning.

reasoning
108 models
MMLU

Massive Multitask Language Understanding benchmark testing knowledge across 57 diverse subjects including STEM, humanities, social sciences, and professional domains

reasoning
99 models
SWE-Bench Verified

A verified subset of 500 software engineering problems from real GitHub issues, validated by human annotators for evaluating language models' ability to resolve real-world coding issues by generating patches for Python codebases.

reasoning
89 models
Humanity's Last Exam

Humanity's Last Exam (HLE) is a multi-modal academic benchmark with 2,500 questions across mathematics, humanities, and natural sciences, designed to test LLM capabilities at the frontier of human knowledge with unambiguous, verifiable solutions

reasoningmultimodal
74 models