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AxonZeta
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Model Details
Model Provider
AxonZeta
Model Tree
deepseek-ai/DeepSeek-V4.1-Flash
Input Modalities
Output Modalities
Supported Functionality
| Benchmark | DeepSeek-V4.1-Flash | AZ-Focus |
|---|---|---|
| SWE-bench Verified, first 100 tasks (mini-SWE-agent, two runs) | 99 / 99 | 99 / 98 |
Thinking on, DeepSeek's recommended sampling, mini-SWE-agent with a 100-step limit, graded with the official harness.
DeepSeek-V4.1-Flash has a reasoning-effort dial from 1 to 100 (default 75). So the fair comparison is at matched settings. Accuracy and mean thinking tokens per item, same questions, same serving setup, one run each:
BIG-Bench Hard (540 tasks)
| Effort | DeepSeek-V4.1-Flash | AZ-Focus | Thinking tokens |
|---|---|---|---|
| 75 (default) | 86.9% · 888 tokens | 84.3% · 393 tokens | 56% less |
| 50 | 87.8% · 514 | 86.7% · 238 | 54% less |
| 35 | 88.0% · 391 | 86.1% · 197 | 50% less |
| 25 | 86.3% · 298 | 87.0% · 189 | 37% less |
MMLU-Pro (500 questions, 14 subjects)
| Effort | DeepSeek-V4.1-Flash | AZ-Focus | Thinking tokens |
|---|---|---|---|
| 75 (default) | 87.2% · 1,692 tokens | 87.4% · 758 tokens | 55% less |
| 50 | 89.2% · 1,175 | 86.8% · 527 | 55% less |
| 35 | 88.4% · 981 | 86.2% · 409 | 58% less |
| 25 | 88.4% · 699 | 85.8% · 356 | 49% less |
Single runs on a few hundred questions move by about a point either way. Across the dial AZ-Focus averages about 1.5 points below the original, for roughly half the thinking. If you need every last point on knowledge questions, the original at effort 50 scored highest in our runs (MMLU-Pro 89.2%). If you want the most answers per GPU, AZ-Focus at a low effort setting is the most efficient point we measured.
| Benchmark | DeepSeek-V4.1-Flash | AZ-Focus | Thinking tokens |
|---|---|---|---|
| GPQA Diamond (198, 2 runs) | 80.8% | 86.7% | 49% less |
| HumanEval (164) | 99.4% | 98.8% | |
| MATH-500 + GSM8K, held-out (450, 3 runs) | 96.2% | 95.9% | 27% less (MATH), 53% less (GSM8K) |
| AIME 2025 (30, 4 runs) | 93.3% | 85.0% |
Thinking on, DeepSeek's recommended sampling (temperature 1.0, top-p 0.95), served with SGLang. None of these benchmarks was used to build the model. GPQA: the original often ran out of its 16k thinking budget; AZ-Focus rarely does.
Use it exactly like deepseek-ai/DeepSeek-V4.1-Flash: 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
by DeepSeek-AI (see LICENSE, DeepSeek_V41_Tech_Report.pdf).
Questions, or want to build on this with us? hello@axonzeta.ai