大模型网摘Agentic Coding Cost Clash: V4.1-Flash-1017 vs GPT-Sol, Claude Opus 5, GLM-5.3, Kimi K3
The headline claim of V4.1-Flash-1017 is cost per task. Using the official DeepSWE v1.1 chart (as reported by The Decoder) plus current API prices, this article examines how models of different sizes compare for agentic coding. Independent benchmarks were not yet published as of this writing.
Per-task cost view
| Model | DeepSWE v1.1 | Avg cost per task |
|---|---|---|
| DeepSeek V4.1-Flash-1017 | 77.0% Pass@1 | ~$0.20 (low setting ~$0.10) |
| DeepSeek V4.1-Flash (prev.) | 74.2% | ~$0.50 |
| GPT-6 Astra / Gemini 3.8 Flash / Claude Opus 5 | all below 77% | ~$2–$10+ |
The official chart also lists the GPT-Sol family, Kimi K3, and GLM-5.3, with Flash-1017 ahead on several coding and agent categories. Caveat: these are vendor-run numbers and cross-vendor methodologies may differ.
Token price reference
At current list prices, deepseek-flash charges $0.15–$0.30 per million cache-miss input tokens and $0.60–$1.20 per million output tokens (half off off-peak); for a line-by-line matchup against GPT-6.1-Sol and GLM-5.3, see Flash vs GPT-6.1-Sol and GLM-5.3. Per-task gaps come mostly from output length and turns per task, not list price alone.
How to choose
- High-concurrency batch work (mass bug fixing, migrations, CI agents): Flash-1017's 2,500 concurrency and ~$0.2 per-task cost stand out;
- Complex architecture, long reasoning chains: consider larger models such as deepseek-v4-pro or Claude Opus 5.5, trading price for success rate;
- Ecosystem lock-in: teams already in Claude Code, Codex, or GLM/Kimi toolchains should run their own regression suite first, following the method in the V4.1-Flash review.
Caveats
Benchmark charts shift with each release; figures here are as of October 19, 2026. For RLSF and technical details see the model deep dive; launch-day coverage is here.