DeepSeek V4.1 Flash 1017 vs Qwen3.6 Max
Full profiles: DeepSeek V4.1 Flash 1017 (DeepSeek) and Qwen3.6 Max (Alibaba): every benchmark, price and rank.
Updated · Data from 7 sources
As of 25 Oct 2026, DeepSeek V4.1 Flash 1017 (max) and Qwen3.6 Max (max) are neck and neck on quality (64.0 vs 62.3 on the BenchLeader Index, inside the ±4.9 margin of error), and DeepSeek V4.1 Flash 1017 (max) costs 5.6× less, which makes it the better buy for most workloads. Pick DeepSeek V4.1 Flash 1017 for coding (70 vs 57); pick Qwen3.6 Max for agents & tools (72 vs 64).
DeepSeek V4.1 Flash 1017 benchmarks max DeepSeek
Qwen3.6 Max benchmarks max AlibabaOpen in the comparison toolWhich to pick for…
The pick's numbers come first. Category scores on the BenchLeader scale (50 is the average model, 15 points one standard deviation). High confidence means a lead of half a standard deviation or more; under one point is called either.
| Use case | Pick | Why | Confidence |
|---|---|---|---|
| Coding | DeepSeek V4.1 Flash 1017 | 70 vs 57 category score; Arena WebDev 1665 vs 1482 | High |
| Agents & tool use | Qwen3.6 Max | 72 vs 64 category score | High |
| Maths | Qwen3.6 Max | 65 vs 62 category score; OTIS Mock AIME 91.1% vs 99.4% | Medium |
| Reasoning | DeepSeek V4.1 Flash 1017 | 71 vs 58 category score; GPQA Diamond 93.2% vs 87.4% | High |
| Knowledge | Qwen3.6 Max | 62 vs 57 category score; SimpleQA Verified 52.0% vs 42.5% | Medium |
| Long documents | DeepSeek V4.1 Flash 1017 | 66 vs 65 category score; AA-LCR 82.7% vs 80.7% | Medium |
| Chat quality (human preference) | – | Only Qwen3.6 Max has an Arena rating (65 category score) | Not comparable |
| Lowest cost | DeepSeek V4.1 Flash 1017 | $0.525 vs $2.96 per 1M blended; agentic coding session $0.023 vs $0.111 | – |
| High volume on a budget | Neither flagship | A smaller model is usually the better fit: Cheapest good LLMs, Fastest LLMs | – |
Key differences
- DeepSeek V4.1 Flash 1017 (max) and Qwen3.6 Max (max) are neck and neck on quality (64.0 vs 62.3, within the ±4.9 margin).
- DeepSeek V4.1 Flash 1017 (max) is stronger in coding, composite, long context, multimodal, reasoning.
- Qwen3.6 Max (max) is stronger in agents & tools, knowledge, maths, human preference, instruction following.
- DeepSeek V4.1 Flash 1017 (max) is 5.6× cheaper ($0.525 vs $2.96 per 1M blended).
At a glance
| Spec | DeepSeek V4.1 Flash 1017 | Qwen3.6 Max |
|---|---|---|
| Developer | DeepSeek | Alibaba |
| Released | 17 Oct 2026 | 20 Apr 2026 |
| Context window (tokens) | 1,048,576 | 245,800 |
| Compared at | DeepSeek V4.1 Flash 1017 (max) | Qwen3.6 Max (max) |
| Open weights | Yes | No |
| Price in / out / cached input per 1M | $0.300 / $1.20 / $0.0060 | $1.32 / $7.90 / $0.132 |
Category by category
Category scores on the BenchLeader scale: 50 is the average evaluated model, 15 points is one standard deviation. A gap under one point is called level.
| Category | DeepSeek V4.1 Flash 1017 (max) | Qwen3.6 Max (max) | Leader |
|---|---|---|---|
| Reasoning | 70.6 | 58.1 | DeepSeek V4.1 Flash 1017 |
| Coding | 69.9 | 56.6 | DeepSeek V4.1 Flash 1017 |
| Agents & tools | 63.5 | 72.1 | Qwen3.6 Max |
| Maths | 61.8 | 64.9 | Qwen3.6 Max |
| Knowledge | 57.1 | 62.3 | Qwen3.6 Max |
| Instruction following | – | 74.6 | not comparable |
| Human preference | – | 64.8 | not comparable |
| Multimodal | 63.7 | – | not comparable |
| Long context | 66.4 | 65.4 | DeepSeek V4.1 Flash 1017 |
| Composite | 80.0 | 61.2 | DeepSeek V4.1 Flash 1017 |
Where sources disagree
Across the 10 benchmarks both have, DeepSeek V4.1 Flash 1017 leads 7, Qwen3.6 Max leads 3. Every overall index that rates both puts DeepSeek V4.1 Flash 1017 ahead.
| Overall index | DeepSeek V4.1 Flash 1017 (max) | Qwen3.6 Max (max) | Leader |
|---|---|---|---|
| BenchLeader Index | 64.0 | 62.3 | neck and neck |
| AA Intelligence Index v4.3.2 | 45.1 | 28.4 | DeepSeek V4.1 Flash 1017 |
Each publisher runs its own tasks, harnesses and effort settings, so their overall verdicts can differ; BenchLeader reconciles them on one scale. † measured at another effort setting of the same model (the publisher did not test the one compared here).
Price, speed and latency
List prices blended at three input tokens per output token. Per-task prices use the token counts on the methodology page; suite cost is what Artificial Analysis paid to run its whole Intelligence Index.
| Measure | DeepSeek V4.1 Flash 1017 (max) | Qwen3.6 Max (max) |
|---|---|---|
| Blended price per 1M tokens | $0.525 | $2.96 |
| Input / output per 1M | $0.300 / $1.20 | $1.32 / $7.90 |
| Cached input per 1M | $0.0060 | $0.132 |
| Price per task: chat reply | $0.0005 | $0.0029 |
| Price per task: summarize a 30-page report | $0.0043 | $0.021 |
| Price per task: code edit | $0.0036 | $0.020 |
| Price per task: agentic coding session | $0.023 | $0.111 |
| Price per task: structured extraction | $0.0008 | $0.0042 |
| Cost to run a full benchmark suite | $0.262 | – |
| Output speed | 195 tok/s | – |
| Time to first answer | 11.4 s | – |
| Time to a complete answer | 13.9 s | – |
Each model is compared at its best-ranked reasoning-effort setting. DeepSeek V4.1 Flash 1017 (max) scores 64.0 and takes 11 s to its first answer token. Qwen3.6 Max (max) scores 62.3. Higher effort means more thinking before the answer: better scores, longer waits and more tokens billed. How reasoning effort changes scores and cost.
Every benchmark
77 benchmarks, grouped by category; each name links to the board and its leaders. † measured at another effort setting of the same model, shown for reference and not counted as a win.
| Metric | Publisher | DeepSeek V4.1 Flash 1017 (max) | Qwen3.6 Max (max) |
|---|---|---|---|
| BenchLeader Index | BenchLeader | 64.0 | 62.3 |
| Agents & tools score | BenchLeader | 63.5 | 72.1 |
| Coding score | BenchLeader | 69.9 | 56.6 |
| Composite score | BenchLeader | 80.0 | 61.2 |
| Knowledge score | BenchLeader | 57.1 | 62.3 |
| Long context score | BenchLeader | 66.4 | 65.4 |
| Maths score | BenchLeader | 61.8 | 64.9 |
| Multimodal score | BenchLeader | 63.7 | – |
| Reasoning score | BenchLeader | 70.6 | 58.1 |
| Human preference score | BenchLeader | – | 64.8 |
| Instruction following score | BenchLeader | – | 74.6 |
| Blended price $/M | BenchLeader | $0.525 | $2.96 |
| Output speed | BenchLeader | 195 tok/s | – |
| Time to first answer | BenchLeader | 11.4 s | – |
| Context window | BenchLeader | 1,048,576 tokens | 245,800 tokens |
| Reasoning | |||
| GPQA Diamond | Epoch AI Benchmarking Hub | 93.2% | 87.4% |
| SimpleBench | SimpleBench | – | 63.0% |
| Arena Hard Prompts | Arena (LMArena) | – | 1483 |
| LiveBench 2.0 Reasoning | LiveBench | 89.1%† | – |
| GPQA Diamond (AA) | Artificial Analysis | – | 88.8% |
| Humanity's Last Exam (AA) | Artificial Analysis | 43.3% | 30.8% |
| ARC-AGI-1 | ARC Prize | 93.5% | – |
| ARC-AGI-2 | ARC Prize | 78.1% | – |
| CritPt | Artificial Analysis | 24.0% | 3.7% |
| Chess Puzzles | Epoch AI Benchmarking Hub | 46.0% | – |
| Mystery Game Puzzles | Epoch AI Benchmarking Hub | 49.0% | 19.0%† |
| LMCA | Epoch AI Benchmarking Hub | – | 42.5% |
| DTBench | Epoch AI Benchmarking Hub | – | 87.2% |
| Coding | |||
| SWE-bench Verified (Epoch) | Epoch AI Benchmarking Hub | – | 76.7% |
| Arena Coding | Arena (LMArena) | – | 1512 |
| Arena WebDev | Arena (LMArena) | 1665 | 1482 |
| LiveBench 2.0 Coding | LiveBench | 96.0%† | – |
| SciCode (AA) | Artificial Analysis | 55.9% | – |
| SWE-bench (Vals) | Vals AI | – | 72.8% |
| Code Migration | Vals AI | 44.2% | – |
| Code Migration v1.1 | Vals AI | 67.5% | – |
| Vibe Code Bench v1.1 | Vals AI | 87.7% | – |
| Terminal-Bench 4.0 (AA) | Artificial Analysis | 33.8% | – |
| FrontierCode 1.1 (AA) | Artificial Analysis | 43.7% | – |
| Agents & tools | |||
| LiveBench Long Agentic | LiveBench | 50.9%† | – |
Show all 77 benchmarks
| Metric | Publisher | DeepSeek V4.1 Flash 1017 (max) | Qwen3.6 Max (max) |
|---|---|---|---|
| Agents & tools | |||
| LiveBench Browser | LiveBench | 76.0%† | – |
| Terminal-Bench Hard | Artificial Analysis | – | 43.9% |
| τ²-Bench Telecom (AA) | Artificial Analysis | – | 95.9% |
| GDPval-AA v2.1 | Artificial Analysis | 62.3% | – |
| Finance Agent v2 | Vals AI | 57.0% | – |
| Finance Agent v2.1 | Vals AI | 62.9% | – |
| Harvey's Legal Agent Benchmark v1.1 | Vals AI | 14.2% | – |
| Harvey's Legal Agent Benchmark v1 | Vals AI | 9.2% | – |
| Legal Research Bench | Vals AI | 45.2% | – |
| Tax Agent Bench | Vals AI | 44.4% | – |
| Terminal-Bench 2.0 (Vals) | Vals AI | – | 51.7% |
| Terminal-Bench 4.0 (Vals) | Vals AI | 31.8% | – |
| Vending-Bench 2 | Andon Labs | – | 4254.2 |
| AutomationBench | Artificial Analysis | 66.6% | – |
| GDP.pdf (AA) | Artificial Analysis | 17.2% | – |
| AA-Briefcase v1.1 | Artificial Analysis | 1570 | – |
| Terminal-Bench-Science 0.1 (AA) | Artificial Analysis | 12.9% | – |
| CWE-Bench (AA) | Artificial Analysis | 69.2% | – |
| DeepSec-Bench (AA) | Artificial Analysis | 24.9% | – |
| CyberGym end-to-end (AA) | Artificial Analysis | 76.3% | – |
| Maths | |||
| FrontierMath Tiers 1–3 | Epoch AI Benchmarking Hub | 71.2% | – |
| FrontierMath Tier 4 | Epoch AI Benchmarking Hub | 46.3% | – |
| OTIS Mock AIME | Epoch AI Benchmarking Hub | 99.4% | 91.1% |
| Arena Maths | Arena (LMArena) | – | 1476 |
| Knowledge | |||
| SimpleQA Verified | Epoch AI Benchmarking Hub | 42.5% | 52.0% |
| LiveBench 2.0 Data Analysis | LiveBench | 79.5%† | – |
| LiveBench Research | LiveBench | 79.5%† | – |
| AA-Omniscience | Artificial Analysis | 4.7 | 9.2 |
| CorpFin | Vals AI | – | 66.5% |
| CaseLaw v2 | Vals AI | – | 47.9% |
| Excel Modeling Benchmark | Vals AI | 61.8% | – |
| Excel Modeling Benchmark v1.1 | Vals AI | 69.2% | – |
| AA-Omniscience: accuracy | Artificial Analysis | 42.9% | 37.9% |
| AA-Omniscience: non-hallucination | Artificial Analysis | 33.3% | 53.8% |
| Instruction following | |||
| IFBench | Artificial Analysis | – | 76.6% |
| Arena Instruction Following | Arena (LMArena) | – | 1452 |
| Human preference | |||
| Arena Text | Arena (LMArena) | – | 1461 |
| Arena Creative Writing | Arena (LMArena) | – | 1438 |
| Arena Multi-turn | Arena (LMArena) | – | 1468 |
| Arena Longer Queries | Arena (LMArena) | – | 1474 |
| Multimodal | |||
| LiveBench Design | LiveBench | 83.4%† | – |
| MMMU-Pro | Artificial Analysis | 80.6% | – |
| Long context | |||
| AA-LCR | Artificial Analysis | 82.7% | 80.7% |
| MLCR | Artificial Analysis | 26.7% | – |
| Composite | |||
| Epoch Capabilities Index | Epoch AI Benchmarking Hub | – | 149.4 |
| LiveBench 2.0 | LiveBench | 79.2%† | – |
| AA Intelligence Index v4.3.2 | Artificial Analysis | 45.1 | 28.4 |
| Vals Index | Vals AI | 61.3 | – |
| Vals Finance Index | Vals AI | 60.9% | – |
| Vals Router Index | Vals AI | 61.3%† | – |
| AA Cyber Index | Artificial Analysis | 56.8 | – |
| Metadata | |||
| AA Openness Index | Artificial Analysis | 44.4 | – |
Data as of 2026-10-25. Best configuration of each model; every score links to its source on the model pages.
What changed
- DeepSeek V4.1 Flash 1017 (max): 10 new results (MLCR, AA Cyber Index, CWE-Bench (AA)…).
- DeepSeek V4.1 Flash 1017 (max): BenchLeader Index 63.1 → 63.9.
- DeepSeek V4.1 Flash 1017 (max): 11 new results (GPQA Diamond, FrontierMath Tiers 1–3, FrontierMath Tier 4…).
- DeepSeek V4.1 Flash 1017 (max): 1 new result (FrontierCode 1.1 (AA)); 2 score revisions.
- Qwen3.6 Max (max): BenchLeader Index 62.8 → 62.3.
- Qwen3.6 Max (max): BenchLeader Index 63.3 → 62.8.
Related comparisons
- DeepSeek V4.1 Flash 1017 vs Claude Opus 5.5
- DeepSeek V4.1 Flash 1017 vs Gemini 4 Argon
- DeepSeek V4.1 Flash 1017 vs Muse Wonder Full
- Qwen3.6 Max vs Claude Opus 5.5
- Qwen3.6 Max vs Gemini 4 Argon
- Qwen3.6 Max vs Muse Wonder Full
- Qwen3.6 Max vs Claude Fable 5.1
- DeepSeek V4.1 Flash 1017 vs GPT-6 Astra
Rankings: Best LLM overall · Best LLM for agents · Best cheap LLMs
DeepSeek V4.1 Flash 1017 vs Qwen3.6 Max: questions
- Is DeepSeek V4.1 Flash 1017 better than Qwen3.6 Max?
- DeepSeek V4.1 Flash 1017 (max) and Qwen3.6 Max (max) are neck and neck on quality (64.0 vs 62.3, within the ±4.9 margin). The BenchLeader Index combines every independent quality benchmark; the two are within the margin of error as of 2026-10-25, so check the category scores for your use.
- Which is cheaper, DeepSeek V4.1 Flash 1017 or Qwen3.6 Max, and will that change?
- DeepSeek V4.1 Flash 1017 is cheaper today: $0.525 against $2.96 per million tokens, blended at three input tokens per output token. No price change has been announced for either.
- Is DeepSeek V4.1 Flash 1017 better than Qwen3.6 Max for coding?
- DeepSeek V4.1 Flash 1017 scores higher in coding (70 vs 57 on the category index, where 50 is average). On Arena WebDev, the top shared coding benchmark, it is 1665 for DeepSeek V4.1 Flash 1017 against 1482 for Qwen3.6 Max.
- Is DeepSeek V4.1 Flash 1017 better than Qwen3.6 Max for agents and tool use?
- Qwen3.6 Max scores higher in agentic tasks (72 vs 64 on the category index, where 50 is average).
- Which is better for long documents?
- DeepSeek V4.1 Flash 1017 scores higher on long-context benchmarks (66 vs 65). DeepSeek V4.1 Flash 1017 accepts more context: 1,048,576 against 245,800 tokens for Qwen3.6 Max.
- Do independent benchmarks agree on DeepSeek V4.1 Flash 1017 vs Qwen3.6 Max?
- Across the 10 benchmarks both have, DeepSeek V4.1 Flash 1017 leads 7, Qwen3.6 Max leads 3. Every overall index that rates both puts DeepSeek V4.1 Flash 1017 ahead.
- When was DeepSeek V4.1 Flash 1017 released?
- DeepSeek V4.1 Flash 1017 was released on 17 Oct 2026.