DeepSeek V4 Pro 0424 vs GPT-5.6 Sol
Full profiles: DeepSeek V4 Pro 0424 (DeepSeek) and GPT-5.6 Sol (OpenAI): every benchmark, price and rank.
Updated · Data from 9 sources
As of 22 Oct 2026, GPT-5.6 Sol (max) leads on quality (68.0 vs 59.7 on the BenchLeader Index), but DeepSeek V4 Pro 0424 (max) costs 15× less, so it is the better buy unless you need the extra quality. Pick DeepSeek V4 Pro 0424 for instruction following (75 vs 71); pick GPT-5.6 Sol for reasoning (73 vs 62).
DeepSeek V4 Pro 0424 benchmarks max DeepSeek
GPT-5.6 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 |
|---|---|---|---|
| Agents & tool use | GPT-5.6 Sol | 67 vs 64 category score; τ³-Banking (AA) 44.3% vs 30.1% | Medium |
| Reasoning | GPT-5.6 Sol | 73 vs 62 category score; CritPt 32.3% vs 12.9% | High |
| Knowledge | GPT-5.6 Sol | 65 vs 57 category score; AA-Omniscience 22.0 vs -10.7 | High |
| Long documents | GPT-5.6 Sol | 67 vs 62 category score; AA-LCR 84.0% vs 74.7% | Medium |
| Following instructions | DeepSeek V4 Pro 0424 | 75 vs 71 category score; IFBench 76.5% vs 72.7% | Medium |
| Lowest cost | DeepSeek V4 Pro 0424 | $0.548 vs $8.00 per 1M blended; agentic coding session $0.030 vs $0.320 | – |
| Fastest first answer | DeepSeek V4 Pro 0424 | 49.0 s (DeepSeek V4 Pro 0424 (max)) vs 90.2 s (GPT-5.6 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-5.6 Sol (max) leads on quality: 68.0 vs 59.7.
- DeepSeek V4 Pro 0424 (max) is stronger in instruction following.
- GPT-5.6 Sol (max) is stronger in agents & tools, composite, knowledge, long context, reasoning, coding, maths, multimodal.
- DeepSeek V4 Pro 0424 (max) is 15× cheaper ($0.548 vs $8.00 per 1M blended).
At a glance
| Spec | DeepSeek V4 Pro 0424 | GPT-5.6 Sol |
|---|---|---|
| Developer | DeepSeek | OpenAI |
| Released | 24 Apr 2026 | 9 Jul 2026 |
| Context window (tokens) | 1,000,000 | 1,050,000 |
| Compared at | DeepSeek V4 Pro 0424 (max) | GPT-5.6 Sol (max) |
| Open weights | Yes | No |
| Price in / out / cached input per 1M | $0.440 / $0.870 / – | $4.00 / $20.00 / $0.400 |
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 Pro 0424 (max) | GPT-5.6 Sol (max) | Leader |
|---|---|---|---|
| Reasoning | 62.2 | 72.5 | GPT-5.6 Sol |
| Coding | – | 65.9 | not comparable |
| Agents & tools | 63.8 | 66.5 | GPT-5.6 Sol |
| Maths | – | 71.0 | not comparable |
| Knowledge | 56.7 | 65.3 | GPT-5.6 Sol |
| Instruction following | 74.5 | 71.1 | DeepSeek V4 Pro 0424 |
| Multimodal | – | 66.6 | not comparable |
| Long context | 62.3 | 67.2 | GPT-5.6 Sol |
| Composite | 63.7 | 82.3 | GPT-5.6 Sol |
Where sources disagree
Across the 21 benchmarks both have, DeepSeek V4 Pro 0424 leads 2, GPT-5.6 Sol leads 19. Every overall index that rates both puts GPT-5.6 Sol ahead.
| Overall index | DeepSeek V4 Pro 0424 (max) | GPT-5.6 Sol (max) | Leader |
|---|---|---|---|
| BenchLeader Index | 59.7 | 68.0 | GPT-5.6 Sol |
| AA Intelligence Index v4.3.2 | 30.4 | 47.0 | GPT-5.6 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 Pro 0424 (max) | GPT-5.6 Sol (max) |
|---|---|---|
| Blended price per 1M tokens | $0.548 | $8.00 |
| Input / output per 1M | $0.440 / $0.870 | $4.00 / $20.00 |
| Cached input per 1M | – | $0.400 |
| Price per task: chat reply | $0.0004 | $0.0076 |
| Price per task: summarize a 30-page report | $0.0058 | $0.060 |
| Price per task: code edit | $0.0039 | $0.054 |
| Price per task: agentic coding session | $0.030 | $0.320 |
| Price per task: structured extraction | $0.0011 | $0.012 |
| Cost to run a full benchmark suite | $0.122 | $1.99 |
| Output speed | 92 tok/s | 81 tok/s |
| Time to first answer | 49.0 s | 90.2 s |
| Time to a complete answer | 54.4 s | 96.4 s |
Per token is not per task: on Artificial Analysis' full suite DeepSeek V4 Pro 0424 (max) cost $0.122 and GPT-5.6 Sol (max) $1.99, because GPT-5.6 Sol (max) used about 1.1× as many tokens for the same work.
Each model is compared at its best-ranked reasoning-effort setting. DeepSeek V4 Pro 0424 (max) scores 59.7 and takes 49 s to its first answer token; DeepSeek V4 Pro 0424 (high) scores 57.9 in 25 s, DeepSeek V4 Pro 0424 (no reasoning) scores 50.8 in 2 s. GPT-5.6 Sol (max) scores 68.0 and takes 90 s to its first answer token; GPT-5.6 Sol (xhigh) scores 66.6 in 36 s, GPT-5.6 Sol (high) scores 66.3 in 9 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
137 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 Pro 0424 (max) | GPT-5.6 Sol (max) |
|---|---|---|---|
| BenchLeader Index | BenchLeader | 59.7 | 68.0 |
| Agents & tools score | BenchLeader | 63.8 | 66.5 |
| Composite score | BenchLeader | 63.7 | 82.3 |
| Instruction following score | BenchLeader | 74.5 | 71.1 |
| Knowledge score | BenchLeader | 56.7 | 65.3 |
| Long context score | BenchLeader | 62.3 | 67.2 |
| Reasoning score | BenchLeader | 62.2 | 72.5 |
| Coding score | BenchLeader | – | 65.9 |
| Maths score | BenchLeader | – | 71.0 |
| Multimodal score | BenchLeader | – | 66.6 |
| Blended price $/M | BenchLeader | $0.548 | $8.00 |
| Output speed | BenchLeader | 92 tok/s | 81 tok/s |
| Time to first answer | BenchLeader | 49.0 s | 90.2 s |
| Context window | BenchLeader | 1,000,000 tokens | 1,050,000 tokens |
| Reasoning | |||
| GPQA Diamond | Epoch AI Benchmarking Hub | – | 93.5% |
| SimpleBench | SimpleBench | – | 64.8%† |
| Arena Hard Prompts | Arena (LMArena) | – | 1511† |
| LiveBench 2.0 Reasoning | LiveBench | – | 91.7%† |
| LiveBench 1.0 Reasoning | LiveBench | – | 91.7% |
| GPQA Diamond (AA) | Artificial Analysis | 88.8% | 94.1% |
| Humanity's Last Exam (AA) | Artificial Analysis | 37.5% | 49.5% |
| GPQA Diamond (Vals) | Vals AI | – | 95.2% |
| EnigmaEval | Scale AI SEAL | – | 37.1%† |
| Kagi LLM Benchmark | Kagi LLM Benchmark | – | 67.0%† |
| ARC-AGI-1 | ARC Prize | – | 96.5% |
| ARC-AGI-2 | ARC Prize | – | 92.5% |
| ARC-AGI-3 | ARC Prize | – | 7.8% |
| CritPt | Artificial Analysis | 12.9% | 32.3% |
| MysteryMechanism | Vals AI | – | 33.3% |
| Chess Puzzles | Epoch AI Benchmarking Hub | – | 59.0% |
| Mystery Game Puzzles | Epoch AI Benchmarking Hub | – | 58.0% |
| LMCA | Epoch AI Benchmarking Hub | – | 58.4% |
| DTBench | Epoch AI Benchmarking Hub | – | 95.5% |
| ForecastBench | ForecastBench | – | 65.5% |
| BTF-3 | Epoch AI Benchmarking Hub | – | 13.7% |
| Coding | |||
| SciCode | SciCode | – | 57.1% |
| WeirdML | WeirdML | – | 87.0% |
| FrontierCode | Cognition | – | 47.5%† |
| GSO-Bench | GSO-Bench | – | 76.5%† |
Show all 137 benchmarks
| Metric | Publisher | DeepSeek V4 Pro 0424 (max) | GPT-5.6 Sol (max) |
|---|---|---|---|
| Coding | |||
| Arena Coding | Arena (LMArena) | – | 1534† |
| Arena WebDev | Arena (LMArena) | – | 1616† |
| LiveBench 2.0 Coding | LiveBench | – | 89.7%† |
| LiveBench 1.0 Coding | LiveBench | – | 83.9% |
| SciCode (AA) | Artificial Analysis | 50.8% | 57.1% |
| LiveCodeBench | Vals AI | – | 82.6% |
| IOI | Vals AI | – | 91.2% |
| SWE-bench (Vals) | Vals AI | – | 96.2% |
| Code Migration | Vals AI | – | 52.9% |
| ProgramBench | Vals AI | – | 1.5% |
| Vibe Code Bench 1-100 | Vals AI | – | 20.0% |
| Vibe Code Bench v1.1 | Vals AI | – | 80.5% |
| SWE Atlas: Codebase QnA | Scale AI SEAL | – | 46.0%† |
| SWE Atlas: Test Writing | Scale AI SEAL | – | 45.9%† |
| MirrorCode | Epoch AI Benchmarking Hub | – | 20.0%† |
| Surface Evolver Bench | Epoch AI Benchmarking Hub | – | 93.1%† |
| DeepSWE v1.1 | Epoch AI Benchmarking Hub | – | 72.7% |
| CursorBench | Cursor | – | 41.7% |
| ALE-Bench | Epoch AI Benchmarking Hub | – | 2176.9 |
| FrontierSWE | Epoch AI Benchmarking Hub | – | 32.2% |
| Terminal-Bench 4.0 (AA) | Artificial Analysis | 14.7% | 39.9% |
| Terminal-Bench 2.1 (AA) | Artificial Analysis | 64.0% | 88.0% |
| Startrise Frontend | Startrise | – | 66.2%† |
| Agents & tools | |||
| Terminal-Bench | Terminal-Bench | – | 37.3% |
| OSWorld-Verified 2.0 | OSWorld | – | 27.3% |
| Arena Agent | Arena (LMArena) | – | 6.6† |
| LiveBench Long Agentic | LiveBench | – | 48.5%† |
| LiveBench Browser | LiveBench | – | 88.0%† |
| LiveBench Agentic Coding | LiveBench | – | 56.2% |
| Terminal-Bench Hard | Artificial Analysis | 46.2% | 65.9% |
| τ²-Bench Telecom (AA) | Artificial Analysis | 96.2% | 85.1% |
| Terminal-Bench 2.1 (Vals) | Vals AI | – | 85.8% |
| HiL-Bench | Scale AI SEAL | – | 32.3%† |
| τ³-bench Banking | τ²-bench | – | 46.9%† |
| GDPval-AA v2.1 | Artificial Analysis | 32.8% | 55.4% |
| τ³-Banking (AA) | Artificial Analysis | 30.1% | 44.3% |
| ITBench SRE (AA) | Artificial Analysis | 38.3% | 56.2% |
| Analyst Agent (AA) | Artificial Analysis | 18.8% | 47.5% |
| APEX-Agents (AA) | Artificial Analysis | 24.3% | – |
| CUA-bench | Vals AI | – | 8.3% |
| CyberBench | Vals AI | – | 76.3% |
| Finance Agent v2 | Vals AI | – | 53.8% |
| Legal Research Bench | Vals AI | – | 48.1% |
| SkillsBench | Vals AI | – | 54.1% |
| SREBench | Vals AI | – | 30.5% |
| Tax Agent Bench | Vals AI | – | 31.1% |
| Terminal-Bench 4.0 (Vals) | Vals AI | – | 37.9% |
| Terminal-Bench Science | Vals AI | – | 20.0% |
| Time Horizon Index: KSP | Vals AI | – | 23.8% |
| Web Search Index | Vals AI | – | 43.6% |
| Arena Search | Arena (LMArena) | – | 1258† |
| BALROG | BALROG | – | 60.0% |
| PostTrainBench | Epoch AI Benchmarking Hub | – | 36.2% |
| GBAEval | Epoch AI Benchmarking Hub | – | 52.6%† |
| Vending-Bench 2 | Andon Labs | – | 9619.4† |
| AutomationBench | Artificial Analysis | 56.3% | 60.1% |
| GDP.pdf (AA) | Artificial Analysis | 13.4% | 27.2% |
| EnterpriseOps-Gym | Artificial Analysis | 40.4% | 42.9% |
| Terminal-Bench-Science 0.1 (AA) | Artificial Analysis | – | 22.4% |
| FACTS Search | – | 88.5%† | |
| Maths | |||
| FrontierMath Tiers 1–3 | Epoch AI Benchmarking Hub | – | 89.1% |
| FrontierMath Tier 4 | Epoch AI Benchmarking Hub | – | 82.9% |
| OTIS Mock AIME | Epoch AI Benchmarking Hub | – | 100.0% |
| ProofBench | Vals AI | – | 83.0% |
| LiveBench Mathematics | LiveBench | – | 96.2% |
| Arena Maths | Arena (LMArena) | – | 1496† |
| Knowledge | |||
| SimpleQA Verified | Epoch AI Benchmarking Hub | – | 69.7% |
| LiveBench 2.0 Data Analysis | LiveBench | – | 79.8%† |
| LiveBench Research | LiveBench | – | 54.5%† |
| LiveBench 1.0 Data Analysis | LiveBench | – | 79.8% |
| AA-Omniscience | Artificial Analysis | -10.7 | 22.0 |
| MMLU-Pro | Vals AI | – | 89.1% |
| LegalBench | Vals AI | – | 87.0% |
| CorpFin | Vals AI | – | 64.4% |
| TaxEval | Vals AI | – | 74.8% |
| PRBench Finance | Scale AI SEAL | – | 50.5% |
| PRBench Legal | Scale AI SEAL | – | 50.5% |
| BioMysteryBench | Vals AI | – | 71.1% |
| Excel Modeling Benchmark | Vals AI | – | 72.3% |
| MedCode | Vals AI | – | 44.0% |
| MedScribe | Vals AI | – | 85.2% |
| Public Benefits Bench | Vals AI | – | 66.5% |
| DrugDiscoveryBench | Scale AI SEAL | – | 56.5% |
| EBR-bench | Epoch AI Benchmarking Hub | – | 44.8% |
| GDP.pdf | Epoch AI Benchmarking Hub | – | 30.7% |
| AA-Omniscience: accuracy | Artificial Analysis | 43.0% | 59.4% |
| AA-Omniscience: non-hallucination | Artificial Analysis | 5.9% | 7.8% |
| Instruction following | |||
| LiveBench Language | LiveBench | – | 87.7% |
| LiveBench Instruction Following | LiveBench | – | 71.8% |
| IFBench | Artificial Analysis | 76.5% | 72.7% |
| Arena Instruction Following | Arena (LMArena) | – | 1491† |
| Human preference | |||
| Arena Text | Arena (LMArena) | – | 1485† |
| EQ-Bench 4 | EQ-Bench | – | 1250† |
| Arena Creative Writing | Arena (LMArena) | – | 1469† |
| Arena Multi-turn | Arena (LMArena) | – | 1490† |
| Arena Longer Queries | Arena (LMArena) | – | 1499† |
| Multimodal | |||
| Arena Vision | Arena (LMArena) | – | 1281† |
| LiveBench Design | LiveBench | – | 81.6%† |
| MMMU-Pro | Artificial Analysis | – | 83.4% |
| MMMU-Pro (Vals) | Vals AI | – | 88.8% |
| MortgageTax | Vals AI | – | 67.3% |
| SAGE | Vals AI | – | 52.6% |
| Blueprint-Bench 2 | Epoch AI Benchmarking Hub | – | 33.6%† |
| Long context | |||
| AA-LCR | Artificial Analysis | 74.7% | 84.0% |
| Arena Document | Arena (LMArena) | – | 1489† |
| MLCR | Artificial Analysis | 9.4% | 30.0% |
| Composite | |||
| Epoch Capabilities Index | Epoch AI Benchmarking Hub | – | 162.8† |
| LiveBench 2.0 | LiveBench | – | 76.3%† |
| LiveBench 1.0 | LiveBench | – | 81.0% |
| AA Intelligence Index v4.3.2 | Artificial Analysis | 30.4 | 47.0 |
| Vals Multimodal Index | Vals AI | – | 72.6% |
| Safety & honesty | |||
| FORTRESS | Scale AI SEAL | – | 20.1%† |
Data as of 2026-10-22. Best configuration of each model; every score links to its source on the model pages.
What changed
- GPT-5.6 Sol (max): 1 score revision.
- GPT-5.6 Sol (max): 1 new result (ForecastBench).
- GPT-5.6 Sol (max): 5 new results (AutomationBench, GDP.pdf (AA), MLCR…).
- GPT-5.6 Sol (max): 1 score revision.
- GPT-5.6 Sol (max): 1 score revision.
- GPT-5.6 Sol (max): 1 new result (PRBench Legal).
- GPT-5.6 Sol (max): 1 new result (DrugDiscoveryBench).
- GPT-5.6 Sol (max): 1 new result (PRBench Legal).
- GPT-5.6 Sol (max): 1 new result (PRBench Finance).
- GPT-5.6 Sol (max): 1 new result (PRBench Finance).
Related comparisons
- DeepSeek V4 Pro 0424 vs Claude Opus 5.5
- DeepSeek V4 Pro 0424 vs Gemini 4 Argon
- DeepSeek V4 Pro 0424 vs Muse Wonder Full
- GPT-5.6 Sol vs Claude Opus 5.5
- GPT-5.6 Sol vs Gemini 4 Argon
- GPT-5.6 Sol vs Muse Wonder Full
- GPT-5.6 Sol vs Claude Fable 5.1
- DeepSeek V4 Pro 0424 vs GPT-6 Astra
Rankings: Best LLM overall · Best LLM for agents · Best cheap LLMs
DeepSeek V4 Pro 0424 vs GPT-5.6 Sol: questions
- Is DeepSeek V4 Pro 0424 better than GPT-5.6 Sol?
- GPT-5.6 Sol (max) leads on quality: 68.0 vs 59.7. The BenchLeader Index combines every independent quality benchmark; GPT-5.6 Sol (max) is ahead overall as of 2026-10-22, but check the category scores for your use.
- Which is cheaper, DeepSeek V4 Pro 0424 or GPT-5.6 Sol, and will that change?
- DeepSeek V4 Pro 0424 is cheaper today: $0.548 against $8.00 per million tokens, blended at three input tokens per output token. No price change has been announced for either.
- Is DeepSeek V4 Pro 0424 better than GPT-5.6 Sol for agents and tool use?
- GPT-5.6 Sol scores higher in agentic tasks (67 vs 64 on the category index, where 50 is average). On τ³-Banking (AA), the top shared agents & tools benchmark, it is 30.1% for DeepSeek V4 Pro 0424 against 44.3% for GPT-5.6 Sol.
- Which is better for long documents?
- GPT-5.6 Sol scores higher on long-context benchmarks (67 vs 62). Both take about 1.1M tokens: DeepSeek V4 Pro 0424 1,000,000, GPT-5.6 Sol 1,050,000.
- Which has lower latency?
- DeepSeek V4 Pro 0424 (max) answers first: 49.0 s against 90.2 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 Pro 0424 or GPT-5.6 Sol?
- DeepSeek V4 Pro 0424 streams faster: 92 against 81 output tokens per second.
- Do independent benchmarks agree on DeepSeek V4 Pro 0424 vs GPT-5.6 Sol?
- Across the 21 benchmarks both have, DeepSeek V4 Pro 0424 leads 2, GPT-5.6 Sol leads 19. Every overall index that rates both puts GPT-5.6 Sol ahead.
- When was GPT-5.6 Sol released?
- GPT-5.6 Sol was released on 9 Jul 2026.