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AxonZeta
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Model Details
Model Provider
AxonZeta
Model Tree
deepseek-ai/DeepSeek-V4.1-Flash-1017
Input Modalities
Output Modalities
Supported Functionality
| Benchmark | DeepSeek-V4.1-Flash-1017 | AZ-Focus |
|---|---|---|
| SWE-bench Verified, first 100 tasks (mini-SWE-agent, two runs) | 94 / 94 | 94 / 93 |
Thinking on, DeepSeek's recommended sampling, mini-SWE-agent with a 100-step limit, graded with the official harness. No empty patches in any run.
| Benchmark | DeepSeek-V4.1-Flash-1017 | AZ-Focus | Thinking tokens |
|---|---|---|---|
| GPQA Diamond (198, 2 runs) | 91.4% · 6,629 tokens | 90.9% · 3,777 tokens | 43% less |
| BIG-Bench Hard (540) | 82.0% · 709 | 84.1% · 359 | 49% less |
| MMLU-Pro (500, 14 subjects) | 89.2% · 1,445 | 88.8% · 953 | 34% less |
| HumanEval (164) | 98.2% · 891 | 98.8% · 552 | 38% less |
| MATH-500 + GSM8K, held-out (450, 3 runs) | 95.0% | 95.0% | 25% less (MATH), 32% less (GSM8K) |
| AIME 2025 (30, 4 runs) | 97.5% · 6,188 | 95.8% · 4,997 | 19% less |
Accuracy and mean thinking tokens per item, same questions, same serving setup. Thinking on, DeepSeek's recommended sampling (temperature 1.0, top-p 0.95), served with SGLang. GPQA and AIME were run with a 128k-token output budget; no AZ-Focus answer came close to it.
DeepSeek-V4.1-Flash-1017 has a reasoning-effort dial from 1 to 100 (default 75), so the fair comparison is at matched settings. One run each:
BIG-Bench Hard (540 tasks)
| Effort | DeepSeek-V4.1-Flash-1017 | AZ-Focus | Thinking tokens |
|---|---|---|---|
| 75 (default) | 82.0% · 709 tokens | 84.1% · 359 tokens | 49% less |
| 50 | 82.8% · 464 | 83.3% · 278 | 40% less |
| 35 | 85.6% · 447 | 81.7% · 242 | 46% less |
| 25 | 83.7% · 300 | 82.8% · 219 | 27% less |
MMLU-Pro (500 questions, 14 subjects)
| Effort | DeepSeek-V4.1-Flash-1017 | AZ-Focus | Thinking tokens |
|---|---|---|---|
| 75 (default) | 89.2% · 1,445 tokens | 88.8% · 953 tokens | 34% less |
| 50 | 88.4% · 1,245 | 87.8% · 799 | 36% less |
| 35 | 87.8% · 1,025 | 89.0% · 657 | 36% less |
| 25 | 88.2% · 704 | 88.2% · 389 | 45% less |
Single runs on a few hundred questions move by a point or two either way. Averaged across the dial, AZ-Focus is level with the original on MMLU-Pro and about half a point lower on BIG-Bench Hard. At effort 25, AZ-Focus answers MMLU-Pro with 45% less thinking than the original at the same setting, at the same accuracy.
Use it exactly like deepseek-ai/DeepSeek-V4.1-Flash-1017:
same serving engines, parallelism, chat template and recommended sampling. The difference shows in thinking mode, and it
stacks with the reasoning-effort setting (chat_template_kwargs: {"reasoning_effort": N} in SGLang). With thinking off,
output length is unchanged: the edit shortens deliberation, not answers.
MIT, as for the original model. Based on
DeepSeek-V4.1-Flash-1017 by DeepSeek-AI (see LICENSE).
Questions, or want to build on this with us? hello@axonzeta.ai