DeepSeek V4.1 Flash 1017 vs GPT-6.1 Sol
Full profiles: DeepSeek V4.1 Flash 1017 (DeepSeek) and GPT-6.1 Sol (OpenAI): every benchmark, price and rank.
Updated · Data from 9 sources
As of 25 Oct 2026, GPT-6.1 Sol (max) leads on quality (69.9 vs 64.0 on the BenchLeader Index), but DeepSeek V4.1 Flash 1017 (max) costs 7.6× less, so it is the better buy unless you need the extra quality. GPT-6.1 Sol leads in every category we measure both in.
DeepSeek V4.1 Flash 1017 benchmarks max DeepSeek
GPT-6.1 Sol benchmarks max OpenAIOpen 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 | GPT-6.1 Sol | 72 vs 70 category score; Arena WebDev 1749 vs 1665 | Medium |
| Agents & tool use | GPT-6.1 Sol | 65 vs 64 category score; Terminal-Bench 4.0 (Vals) 55.0% vs 31.8% | Medium |
| Maths | GPT-6.1 Sol | 73 vs 62 category score; FrontierMath Tiers 1–3 93.7% vs 71.2% | High |
| Reasoning | GPT-6.1 Sol | 76 vs 71 category score; GPQA Diamond 95.4% vs 93.2% | Medium |
| Knowledge | GPT-6.1 Sol | 78 vs 57 category score; SimpleQA Verified 73.9% vs 42.5% | High |
| Long documents | Either | 66 vs 67 category score; AA-LCR 82.7% vs 83.0% | Either |
| Images and other media | GPT-6.1 Sol | 66 vs 64 category score; MMMU-Pro 86.0% vs 80.6% | Medium |
| Chat quality (human preference) | – | Only GPT-6.1 Sol has an Arena rating (67 category score) | Not comparable |
| Lowest cost | DeepSeek V4.1 Flash 1017 | $0.525 vs $4.00 per 1M blended; agentic coding session $0.023 vs $0.160 | – |
| Fastest first answer | DeepSeek V4.1 Flash 1017 | 11.4 s (DeepSeek V4.1 Flash 1017 (max)) vs 162.1 s (GPT-6.1 Sol (max)) to the first answer token | – |
| High volume on a budget | Neither flagship | A smaller model is usually the better fit: Cheapest good LLMs, Fastest LLMs | – |
Key differences
- GPT-6.1 Sol (max) leads on quality: 69.9 vs 64.0.
- GPT-6.1 Sol (max) is stronger in agents & tools, coding, composite, knowledge, long context, maths, multimodal, reasoning, human preference.
- DeepSeek V4.1 Flash 1017 (max) is 7.6× cheaper ($0.525 vs $4.00 per 1M blended).
- DeepSeek V4.1 Flash 1017 (max) streams 3.4× faster (195 vs 57 tokens per second).
At a glance
| Spec | DeepSeek V4.1 Flash 1017 | GPT-6.1 Sol |
|---|---|---|
| Developer | DeepSeek | OpenAI |
| Released | 17 Oct 2026 | 29 Sept 2026 |
| Context window (tokens) | 1,048,576 | 1,050,000 |
| Compared at | DeepSeek V4.1 Flash 1017 (max) | GPT-6.1 Sol (max) |
| Open weights | Yes | No |
| Price in / out / cached input per 1M | $0.300 / $1.20 / $0.0060 | $2.00 / $10.00 / $0.100 |
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) | GPT-6.1 Sol (max) | Leader |
|---|---|---|---|
| Reasoning | 70.6 | 76.0 | GPT-6.1 Sol |
| Coding | 69.9 | 71.9 | GPT-6.1 Sol |
| Agents & tools | 63.5 | 64.5 | GPT-6.1 Sol |
| Maths | 61.8 | 73.0 | GPT-6.1 Sol |
| Knowledge | 57.1 | 78.4 | GPT-6.1 Sol |
| Human preference | – | 67.2 | not comparable |
| Multimodal | 63.7 | 66.1 | GPT-6.1 Sol |
| Long context | 66.4 | 66.6 | level |
| Composite | 80.0 | 87.5 | GPT-6.1 Sol |
Where sources disagree
Across the 39 benchmarks both have, DeepSeek V4.1 Flash 1017 leads 11, GPT-6.1 Sol leads 28. Every overall index that rates both puts GPT-6.1 Sol ahead.
| Overall index | DeepSeek V4.1 Flash 1017 (max) | GPT-6.1 Sol (max) | Leader |
|---|---|---|---|
| BenchLeader Index | 64.0 | 69.9 | GPT-6.1 Sol |
| AA Intelligence Index v4.3.2 | 45.1 | 51.8 | GPT-6.1 Sol |
| Vals Index | 61.3 | 63.9 | GPT-6.1 Sol |
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) | GPT-6.1 Sol (max) |
|---|---|---|
| Blended price per 1M tokens | $0.525 | $4.00 |
| Input / output per 1M | $0.300 / $1.20 | $2.00 / $10.00 |
| Cached input per 1M | $0.0060 | $0.100 |
| Price per task: chat reply | $0.0005 | $0.0038 |
| Price per task: summarize a 30-page report | $0.0043 | $0.030 |
| Price per task: code edit | $0.0036 | $0.027 |
| Price per task: agentic coding session | $0.023 | $0.160 |
| Price per task: structured extraction | $0.0008 | $0.0060 |
| Cost to run a full benchmark suite | $0.262 | $0.724 |
| Output speed | 195 tok/s | 57 tok/s |
| Time to first answer | 11.4 s | 162.1 s |
| Time to a complete answer | 13.9 s | 170.8 s |
Per token is not per task: on Artificial Analysis' full suite DeepSeek V4.1 Flash 1017 (max) cost $0.262 and GPT-6.1 Sol (max) $0.724, because DeepSeek V4.1 Flash 1017 (max) used about 2.8× as many tokens for the same work, which a per-token price hides.
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. GPT-6.1 Sol (max) scores 69.9 and takes 162 s to its first answer token; GPT-6.1 Sol (high) scores 67.7 in 31 s, GPT-6.1 Sol (xhigh) scores 67.7 in 60 s. 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
111 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) | GPT-6.1 Sol (max) |
|---|---|---|---|
| BenchLeader Index | BenchLeader | 64.0 | 69.9 |
| Agents & tools score | BenchLeader | 63.5 | 64.5 |
| Coding score | BenchLeader | 69.9 | 71.9 |
| Composite score | BenchLeader | 80.0 | 87.5 |
| Knowledge score | BenchLeader | 57.1 | 78.4 |
| Long context score | BenchLeader | 66.4 | 66.6 |
| Maths score | BenchLeader | 61.8 | 73.0 |
| Multimodal score | BenchLeader | 63.7 | 66.1 |
| Reasoning score | BenchLeader | 70.6 | 76.0 |
| Human preference score | BenchLeader | – | 67.2 |
| Blended price $/M | BenchLeader | $0.525 | $4.00 |
| Output speed | BenchLeader | 195 tok/s | 57 tok/s |
| Time to first answer | BenchLeader | 11.4 s | 162.1 s |
| Context window | BenchLeader | 1,048,576 tokens | 1,050,000 tokens |
| Reasoning | |||
| GPQA Diamond | Epoch AI Benchmarking Hub | 93.2% | 95.4% |
| Arena Hard Prompts | Arena (LMArena) | – | 1504 |
| LiveBench 2.0 Reasoning | LiveBench | 89.1%† | 92.6%† |
| LiveBench 1.0 Reasoning | LiveBench | – | 92.6% |
| Humanity's Last Exam (AA) | Artificial Analysis | 43.3% | 52.9% |
| ARC-AGI-1 | ARC Prize | 93.5% | 96.5% |
| ARC-AGI-2 | ARC Prize | 78.1% | 94.2% |
| ARC-AGI-3 | ARC Prize | – | 96.2% |
| CritPt | Artificial Analysis | 24.0% | 31.7% |
| MysteryMechanism | Vals AI | – | 46.4% |
| Chess Puzzles | Epoch AI Benchmarking Hub | 46.0% | 68.0% |
| Mystery Game Puzzles | Epoch AI Benchmarking Hub | 49.0% | 80.0% |
| LMCA | Epoch AI Benchmarking Hub | – | 62.8% |
| DTBench | Epoch AI Benchmarking Hub | – | 96.5% |
| Coding | |||
| SciCode | SciCode | – | 54.2% |
| FrontierCode | Cognition | – | 50.2%† |
| Arena Coding | Arena (LMArena) | – | 1537 |
| Arena WebDev | Arena (LMArena) | 1665 | 1749 |
| LiveBench 2.0 Coding | LiveBench | 96.0%† | 96.0%† |
| LiveBench 1.0 Coding | LiveBench | – | 80.4% |
| SciCode (AA) | Artificial Analysis | 55.9% | 54.2% |
| IOI | Vals AI | – | 96.9% |
| Code Migration | Vals AI | 44.2% | 65.1% |
| Code Migration v1.1 | Vals AI | 67.5% | 77.3% |
| ProgramBench | Vals AI | – | 3.0% |
Show all 111 benchmarks
| Metric | Publisher | DeepSeek V4.1 Flash 1017 (max) | GPT-6.1 Sol (max) |
|---|---|---|---|
| Coding | |||
| SWE-bench Multimodal | Vals AI | – | 43.5% |
| Vibe Code Bench v1.1 | Vals AI | 87.7% | 88.9% |
| ALE-Bench | Epoch AI Benchmarking Hub | – | 2743.8 |
| Terminal-Bench 4.0 (AA) | Artificial Analysis | 33.8% | 56.1% |
| Startrise Frontend | Startrise | – | 78.6%† |
| FrontierCode 1.1 (AA) | Artificial Analysis | 43.7% | 49.1% |
| Agents & tools | |||
| Terminal-Bench | Terminal-Bench | – | 58.2% |
| APEX-Agents | Mercor | – | 60.0% |
| Arena Agent | Arena (LMArena) | – | 13.6 |
| LiveBench Long Agentic | LiveBench | 50.9%† | 73.0%† |
| LiveBench Browser | LiveBench | 76.0%† | 88.0%† |
| LiveBench Agentic Coding | LiveBench | – | 54.5% |
| HiL-Bench | Scale AI SEAL | – | 62.3%† |
| GDPval-AA v2.1 | Artificial Analysis | 62.3% | 54.8% |
| Analyst Agent (AA) | Artificial Analysis | – | 50.0% |
| CyberBench | Vals AI | – | 39.3% |
| Finance Agent v2 | Vals AI | 57.0% | 52.0% |
| Finance Agent v2.1 | Vals AI | 62.9% | 61.8% |
| Harvey's Legal Agent Benchmark v1.1 | Vals AI | 14.2% | 6.3% |
| Harvey's Legal Agent Benchmark v1 | Vals AI | 9.2% | 5.4% |
| Legal Research Bench | Vals AI | 45.2% | 38.5% |
| Robo-bench | Vals AI | – | 28.3% |
| SREBench | Vals AI | – | 50.8% |
| Tax Agent Bench | Vals AI | 44.4% | 21.8% |
| Terminal-Bench 4.0 (Vals) | Vals AI | 31.8% | 55.0% |
| Terminal-Bench Science | Vals AI | – | 52.9% |
| Arena Search | Arena (LMArena) | – | 1250 |
| AutomationBench | Artificial Analysis | 66.6% | 64.9% |
| GDP.pdf (AA) | Artificial Analysis | 17.2% | 31.0% |
| Harvey LAB-AA v1.1 | Harvey | – | 6.9% |
| AA-Briefcase v1.1 | Artificial Analysis | 1570 | 1555 |
| Terminal-Bench-Science 0.1 (AA) | Artificial Analysis | 12.9% | 58.1% |
| CWE-Bench (AA) | Artificial Analysis | 69.2% | – |
| DeepSec-Bench (AA) | Artificial Analysis | 24.9% | – |
| CyberGym end-to-end (AA) | Artificial Analysis | 76.3% | – |
| FACTS Search | – | 88.2%† | |
| Maths | |||
| FrontierMath Tiers 1–3 | Epoch AI Benchmarking Hub | 71.2% | 93.7% |
| FrontierMath Tier 4 | Epoch AI Benchmarking Hub | 46.3% | 100.0% |
| OTIS Mock AIME | Epoch AI Benchmarking Hub | 99.4% | 100.0% |
| ProofBench | Vals AI | – | 99.0%† |
| LiveBench Mathematics | LiveBench | – | 96.8% |
| Arena Maths | Arena (LMArena) | – | 1513 |
| Knowledge | |||
| SimpleQA Verified | Epoch AI Benchmarking Hub | 42.5% | 73.9% |
| LiveBench 2.0 Data Analysis | LiveBench | 79.5%† | 82.7%† |
| LiveBench Research | LiveBench | 79.5%† | 83.5%† |
| LiveBench 1.0 Data Analysis | LiveBench | – | 82.7% |
| AA-Omniscience | Artificial Analysis | 4.7 | 41.5 |
| PRBench Finance | Scale AI SEAL | – | 46.2%† |
| PRBench Legal | Scale AI SEAL | – | 47.9%† |
| BioMysteryBench | Vals AI | – | 79.6% |
| ClaimBench | Vals AI | – | 100.0% |
| Excel Modeling Benchmark | Vals AI | 61.8% | 70.8% |
| Excel Modeling Benchmark v1.1 | Vals AI | 69.2% | 71.1% |
| MedCode | Vals AI | – | 48.8% |
| MedScribe | Vals AI | – | 86.5% |
| Public Benefits Bench v1.1 | Vals AI | – | 59.3% |
| EBR-bench | Epoch AI Benchmarking Hub | – | 54.3% |
| GDP.pdf | Epoch AI Benchmarking Hub | – | 35.0% |
| AA-Omniscience: accuracy | Artificial Analysis | 42.9% | 62.1% |
| AA-Omniscience: non-hallucination | Artificial Analysis | 33.3% | 45.7% |
| Instruction following | |||
| LiveBench Language | LiveBench | – | 90.1% |
| LiveBench Instruction Following | LiveBench | – | 74.2% |
| MultiChallenge | Scale AI SEAL | – | 82.0%† |
| Arena Instruction Following | Arena (LMArena) | – | 1485 |
| Human preference | |||
| Arena Text | Arena (LMArena) | – | 1480 |
| Arena Creative Writing | Arena (LMArena) | – | 1453 |
| Arena Multi-turn | Arena (LMArena) | – | 1483 |
| Arena Longer Queries | Arena (LMArena) | – | 1489 |
| Multimodal | |||
| Arena Vision | Arena (LMArena) | – | 1288 |
| LiveBench Design | LiveBench | 83.4%† | 82.1%† |
| MMMU-Pro | Artificial Analysis | 80.6% | 86.0% |
| SAGE | Vals AI | – | 46.5% |
| AA-3D | Artificial Analysis | – | 1119 |
| Long context | |||
| AA-LCR | Artificial Analysis | 82.7% | 83.0% |
| MLCR | Artificial Analysis | 26.7% | 38.3% |
| Composite | |||
| Epoch Capabilities Index | Epoch AI Benchmarking Hub | – | 168.4† |
| LiveBench 2.0 | LiveBench | 79.2%† | 85.4%† |
| LiveBench 1.0 | LiveBench | – | 81.6% |
| AA Intelligence Index v4.3.2 | Artificial Analysis | 45.1 | 51.8 |
| Vals Index | Vals AI | 61.3 | 63.9 |
| Vals Finance Index | Vals AI | 60.9% | 55.8% |
| Vals RSI Index | Vals AI | – | 26.6% |
| Vals Science Index | Vals AI | – | 59.6% |
| Vals Router Index | Vals AI | 61.3%† | 63.9%† |
| 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)…).
- GPT-6.1 Sol (max): 10 new results (ClaimBench, Code Migration v1.1, Excel Modeling Benchmark v1.1…).
- 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…).
- GPT-6.1 Sol (max): 5 score revisions.
- DeepSeek V4.1 Flash 1017 (max): 1 new result (FrontierCode 1.1 (AA)); 2 score revisions.
- GPT-6.1 Sol (max): 2 score revisions.
- GPT-6.1 Sol (max): BenchLeader Index 69.3 → 70.0.
- GPT-6.1 Sol (max): 2 new results (LMCA, DTBench).
- GPT-6.1 Sol (max): 2 score revisions.
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
- GPT-6.1 Sol vs Claude Opus 5.5
- GPT-6.1 Sol vs Gemini 4 Argon
- GPT-6.1 Sol vs Muse Wonder Full
- GPT-6.1 Sol vs Claude Fable 5.1
- DeepSeek V4 Pro vs GPT-6.1 Sol
Rankings: Best LLM overall · Best LLM for coding · Best LLM for agents · Best cheap LLMs
DeepSeek V4.1 Flash 1017 vs GPT-6.1 Sol: questions
- Is DeepSeek V4.1 Flash 1017 better than GPT-6.1 Sol?
- GPT-6.1 Sol (max) leads on quality: 69.9 vs 64.0. The BenchLeader Index combines every independent quality benchmark; GPT-6.1 Sol (max) is ahead overall as of 2026-10-25, but check the category scores for your use.
- Which is cheaper, DeepSeek V4.1 Flash 1017 or GPT-6.1 Sol, and will that change?
- DeepSeek V4.1 Flash 1017 is cheaper today: $0.525 against $4.00 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 GPT-6.1 Sol for coding?
- GPT-6.1 Sol scores higher in coding (72 vs 70 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 1749 for GPT-6.1 Sol.
- Is DeepSeek V4.1 Flash 1017 better than GPT-6.1 Sol for agents and tool use?
- GPT-6.1 Sol scores higher in agentic tasks (65 vs 64 on the category index, where 50 is average).
- Which is better for long documents?
- They are level on long-context benchmarks (66 vs 67). Both take about 1.1M tokens: DeepSeek V4.1 Flash 1017 1,048,576, GPT-6.1 Sol 1,050,000.
- Which has lower latency?
- DeepSeek V4.1 Flash 1017 (max) answers first: 11.4 s against 162.1 s to the first answer token, as Artificial Analysis measures it. Reasoning effort drives most of this: a higher setting thinks longer before it answers.
- Which is faster, DeepSeek V4.1 Flash 1017 or GPT-6.1 Sol?
- DeepSeek V4.1 Flash 1017 streams faster: 195 against 57 output tokens per second.
- Do independent benchmarks agree on DeepSeek V4.1 Flash 1017 vs GPT-6.1 Sol?
- Across the 39 benchmarks both have, DeepSeek V4.1 Flash 1017 leads 11, GPT-6.1 Sol leads 28. Every overall index that rates both puts GPT-6.1 Sol ahead.
- When was DeepSeek V4.1 Flash 1017 released?
- DeepSeek V4.1 Flash 1017 was released on 17 Oct 2026.