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DeepSWE

Benchmark Covered in 11 stories + Follow

DeepSWE is referenced in recent coverage as a software engineering benchmark used to measure and compare coding and agentic model performance across multiple languages and task types. The stories report DeepSWE results in head-to-head evaluations and cost-vs-accuracy studies, including multi-model “cascades” and routing strategies that start with a lower-cost model and escalate on failures to improve solve rates.

Updated 17 September 2026

Latest developments

Timeline

Month Quarter Year

September 2026

Sakana AI releases Fugu Max and Fugu Ultra v2 multi-agent orchestration models via a hosted, OpenAI-compatible API Model release

Meta released Muse Spark 1.3, an agentic coding model with efficiency improvements and new availability via Muse Code and the Meta Model API Model release

August 2026

Together AI reported DeepSWE benchmark results comparing GLM-5.3 with GPT-5.6 Sol and Claude Fable 5, including a proposed two-model routing approach Benchmark result

DeepSeek V4 Pro benchmarked against Claude Fable 5 and GPT-5.6 Sol on DeepSWE software engineering tasks Benchmark result

Google launches Gemini 3.7 Flash, a coding-and-agent model offered via its Gemini API at $0.75 per 1M input tokens and $3.75 per 1M output tokens through year-end Model release

Meta releases Muse Code terminal agent and Muse Spark 1.2 coding model Model release

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