DeepSeek V4.1 Flash-1017 Arrives: Cheaper, Faster, and Not Quite Opus 5
DeepSeek released the V4.1 Flash-1017 snapshot with MIT-licensed weights and low API pricing. Its own benchmark table does not back the on-par-with-Opus-5 chatter.
DeepSeek released DeepSeek-V4.1-Flash-1017 on October 17, 2026, which it calls a "minor update" to V4.1 Flash. The company says it delivers "substantial improvements" across reasoning, coding, agentic tasks and visual benchmarks, while cutting task execution time and output length. The weights are MIT licensed.
The model card's scores: 42.6 on HLE without tools (44.0 on the text-only subset), 42.3 on Terminal-Bench 4.0, 77.0 on DeepSWE v1.1, 29.1 on ProgramBench and 68.2 on NL2Repo-Bench. Terminal-Bench ran on the Claude Code harness, DeepSWE on Mini-SWE-V2. The release material does not say how many parameters the mixture-of-experts model has.
Pricing is the headline. OpenRouter lists the model at $0.15 per million prompt tokens and $0.60 per million completion tokens, with a 1,048,576-token context window, under the API name deepseek-flash.
Then there is the chatter. Commentator @kimmonismus wrote that it is "roughly on par with Opus 5" and beats GPT-5.6 Sol. DeepSeek's own table disagrees. Opus 5 scores 56.3 on HLE without tools against 42.6 here, and 51.8 against 42.3 on Terminal-Bench 4.0. GPT-5.6 Sol also edges it on HLE, 44.5 to 42.6.
My take: a cheap, open model with these scores is a real result. "On par" is a mood. Show me the eval, not the vibe.
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Meanwhile at the anchor desk
Fifteen cents per million prompt tokens for a million-token context window? Darling, that is not a price list, that is a clearance event with a benchmark table attached!
MIT weights, 1M context, $0.60 per million output tokens. No Jinja chat template, so use the encoding.py reference or the deepseek-recipe Rust toolkit.
