DeepSeek V4 Pro vs GPT-6 Astra
Full profiles: DeepSeek V4 Pro (DeepSeek) and GPT-6 Astra (OpenAI): every benchmark, price and rank.
Updated · Data from 12 sources
As of 9 Nov 2026, GPT-6 Astra is ahead of DeepSeek V4 Pro on quality (68.1 vs 59.1 on the BenchLeader Index), but DeepSeek V4 Pro is 20× cheaper at $0.990 vs $20.00 per 1M tokens blended. GPT-6 Astra leads in every category we measure both in.
Key facts (as of 9 Nov 2026)
- DeepSeek V4 Pro scores 59.1 on the BenchLeader Index and GPT-6 Astra scores 68.1 (BenchLeader, 9 Nov 2026).
- DeepSeek V4 Pro costs $0.990 per 1M tokens blended (list price, 9 Nov 2026).
- GPT-6 Astra costs $20.00 per 1M tokens blended (list price, 9 Nov 2026).
Which 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 Astra | 83 vs 54 category score | High |
| Maths | GPT-6 Astra | 78 vs 65 category score | High |
| Reasoning | GPT-6 Astra | 86 vs 62 category score | High |
| Chat quality (human preference) | – | Only DeepSeek V4 Pro has an Arena rating (65 category score) | Not comparable |
| Lowest cost | DeepSeek V4 Pro | $0.990 vs $20.00 per 1M blended; agentic coding session $0.048 vs $0.800 | – |
| Fastest first answer | DeepSeek V4 Pro | 1.5 s (DeepSeek V4 Pro (high)) vs 4.8 s (GPT-6 Astra) 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 Astra leads on quality: 68.1 vs 59.1.
- DeepSeek V4 Pro (high) is stronger in human preference.
- GPT-6 Astra is stronger in coding, maths, reasoning, agents & tools, composite, knowledge.
- DeepSeek V4 Pro (high) is 20× cheaper ($0.990 vs $20.00 per 1M blended).
- DeepSeek V4 Pro (high) streams 1.6× faster (44 vs 27 tokens per second).
At a glance
| Spec | DeepSeek V4 Pro | GPT-6 Astra |
|---|---|---|
| Developer | DeepSeek | OpenAI |
| Released | 13 Aug 2026 | 2 Sep 2026 |
| Context window (tokens) | 1,048,576 | 1,050,000 |
| Compared at | DeepSeek V4 Pro (high) | GPT-6 Astra |
| Open weights | Yes | No |
| Price in / out / cached input per 1M | $0.660 / $1.98 / $0.022 | $10.00 / $50.00 / $1.00 |
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 (high) | GPT-6 Astra | Leader |
|---|---|---|---|
| Reasoning | 62.2 | 85.6 | GPT-6 Astra |
| Coding | 53.8 | 83.2 | GPT-6 Astra |
| Agents & tools | – | 69.2 | not comparable |
| Maths | 64.6 | 77.9 | GPT-6 Astra |
| Knowledge | – | 56.1 | not comparable |
| Human preference | 65.1 | – | not comparable |
| Composite | – | 72.6 | not comparable |
On one chart
Each domain on the index's own scale, where 50 is an average model. Click a domain to see the benchmarks behind it. Pick up to 5 models from the list.
Not drawn, measured for fewer than two of these models: Agents & tools, Knowledge, Instruction following, Long context, Multimodal, Human preference.
Where sources disagree
Across the 0 benchmarks both have, DeepSeek V4 Pro leads 0, GPT-6 Astra leads 0. Every overall index that rates both puts GPT-6 Astra ahead.
| Overall index | DeepSeek V4 Pro (high) | GPT-6 Astra | Leader |
|---|---|---|---|
| BenchLeader Index | 59.1 | 68.1 | GPT-6 Astra |
| AA Intelligence Index v5.0 | 12.6† | 43.3 | GPT-6 Astra |
| Vals Index | 53.0† | 65.0 | GPT-6 Astra |
| Arena Text | 1464 | 1475† | GPT-6 Astra |
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. Cost, tokens and time per benchmark task are measured: Artificial Analysis’ averages over its whole Intelligence Index run.
| Measure | DeepSeek V4 Pro (high) | GPT-6 Astra |
|---|---|---|
| Blended price per 1M tokens | $0.990 | $20.00 |
| Input / output per 1M | $0.660 / $1.98 | $10.00 / $50.00 |
| Cached input per 1M | $0.022 | $1.00 |
| Price per task: chat reply | $0.0009 | $0.019 |
| Price per task: summarize a 30-page report | $0.0091 | $0.150 |
| Price per task: code edit | $0.0069 | $0.135 |
| Price per task: agentic coding session | $0.048 | $0.800 |
| Price per task: structured extraction | $0.0017 | $0.030 |
| Agentic coding session, input mostly cached | $0.019 | $0.395 |
| Output speed | 44 tok/s | 27 tok/s |
| Time to first answer | 1.5 s | 4.8 s |
Each model is compared at its best-ranked reasoning-effort setting. DeepSeek V4 Pro (high) scores 59.1 and takes 1 s to its first answer token; DeepSeek V4 Pro (max) scores 56.9 in 74 s, DeepSeek V4 Pro scores 53.3 in 1 s. GPT-6 Astra scores 68.1 and takes 5 s to its first answer token; GPT-6 Astra (max) scores 67.4 in 232 s, GPT-6 Astra (high) scores 66.5 in 64 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
167 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 (high) | GPT-6 Astra |
|---|---|---|---|
| BenchLeader Index | BenchLeader | 59.1 | 68.1 |
| Coding score | BenchLeader | 53.8 | 83.2 |
| Human preference score | BenchLeader | 65.1 | – |
| Maths score | BenchLeader | 64.6 | 77.9 |
| Reasoning score | BenchLeader | 62.2 | 85.6 |
| Agents & tools score | BenchLeader | – | 69.2 |
| Composite score | BenchLeader | – | 72.6 |
| Knowledge score | BenchLeader | – | 56.1 |
| Blended price $/M | BenchLeader | $0.990 | $20.00 |
| Output speed | BenchLeader | 44 tok/s | 27 tok/s |
| Time to first answer | BenchLeader | 1.5 s | 4.8 s |
| Context window | BenchLeader | 1,048,576 tokens | 1,050,000 tokens |
| Reasoning | |||
| GPQA Diamond | Epoch AI Benchmarking Hub | 90.9% | 95.8%† |
| Humanity's Last Exam | Scale AI / CAIS | – | 54.8% |
| Arena Hard Prompts | Arena (LMArena) | 1482 | 1500† |
| LiveBench 2.0 Reasoning | LiveBench | – | 92.7% |
| LiveBench 1.0 Reasoning | LiveBench | 85.8%† | 92.7%† |
| GPQA Diamond (AA) | Artificial Analysis | 92.8%† | 96.1%† |
| Humanity's Last Exam (AA) | Artificial Analysis | 41.0%† | 54.7%† |
| HLE-Diamond (AA) | Artificial Analysis | 19.5%† | 63.8%† |
| GPQA Diamond (Vals) | Vals AI | 92.4%† | – |
| Kagi LLM Benchmark | Kagi LLM Benchmark | 53.5%† | – |
| ARC-AGI-1 | ARC Prize | 87.2% | 97.5%† |
| ARC-AGI-2 | ARC Prize | 59.7% | 95.0%† |
| ARC-AGI-3 | ARC Prize | – | 98.6%† |
| CritPt | Artificial Analysis | 18.0%† | 31.7%† |
| CritPt v2 hard subset (AA) | Artificial Analysis | 23.5%† | 70.5%† |
| MysteryMechanism | Vals AI | – | 53.1%† |
| Chess Puzzles | Epoch AI Benchmarking Hub | 16.0% | 74.0%† |
| Mystery Game Puzzles | Epoch AI Benchmarking Hub | 43.0%† | 84.0%† |
| CL-bench Life | Epoch AI Benchmarking Hub | 13.5% | – |
| LMCA | Epoch AI Benchmarking Hub | 45.5%† | 64.1% |
| DTBench | Epoch AI Benchmarking Hub | 93.9%† | 97.1% |
| ForecastBench | ForecastBench | 60.7%† | – |
| BTF-3 | Epoch AI Benchmarking Hub | – | 13.5%† |
| Coding | |||
| SWE-bench Verified (Epoch) | Epoch AI Benchmarking Hub | 77.6%† | – |
| SciCode | SciCode | 46.4% | 56.5%† |
Show all 167 benchmarks
| Metric | Publisher | DeepSeek V4 Pro (high) | GPT-6 Astra |
|---|---|---|---|
| Coding | |||
| WeirdML | WeirdML | 46.5% | 93.3%† |
| FrontierCode | Cognition | 28.6% | 53.3%† |
| GSO-Bench | GSO-Bench | – | 79.4% |
| Arena Coding | Arena (LMArena) | 1503 | 1548† |
| Arena WebDev | Arena (LMArena) | 1581 | 1767† |
| LiveBench 2.0 Coding | LiveBench | – | 87.5% |
| LiveBench 1.0 Coding | LiveBench | 77.2%† | 80.4%† |
| SciCode (AA) | Artificial Analysis | 51.0%† | 56.5%† |
| LiveCodeBench | Vals AI | 87.5%† | – |
| IOI | Vals AI | 51.6%† | 100.0%† |
| SWE-bench (Vals) | Vals AI | 96.4%† | – |
| Code Migration | Vals AI | 41.5%† | 67.7%† |
| Code Migration v1.1 | Vals AI | 52.0%† | 80.6%† |
| ProgramBench | Vals AI | 0.0%† | 5.5%† |
| SWE-bench Multimodal | Vals AI | – | 43.3%† |
| Vibe Code Bench 1-100 | Vals AI | 17.5%† | 27.6%† |
| Vibe Code Bench v1.1 | Vals AI | 82.3%† | 89.6%† |
| SWE Atlas: Codebase QnA | Scale AI SEAL | 27.1%† | 59.1%† |
| SWE Atlas: Refactoring | Scale AI SEAL | – | 59.0%† |
| SWE Atlas: Test Writing | Scale AI SEAL | 27.1%† | 50.7%† |
| MirrorCode | Epoch AI Benchmarking Hub | – | 46.7%† |
| Surface Evolver Bench | Epoch AI Benchmarking Hub | 40.0% | – |
| DeepSWE v1.1 | Epoch AI Benchmarking Hub | – | 73.2%† |
| ALE-Bench | Epoch AI Benchmarking Hub | 1006.1 | 2951.3† |
| FrontierSWE | Epoch AI Benchmarking Hub | – | 65.5%† |
| Terminal-Bench 4.0 (AA) | Artificial Analysis | 14.1%† | 59.1%† |
| Terminal-Bench 2.1 (AA) | Artificial Analysis | 78.7%† | 88.4%† |
| Startrise Frontend | Startrise | 62.7%† | 79.9% |
| GameBench 2 Overall | DuelLab GameBench 2 | 42.0† | 88.7 |
| GameBench 2 by reasoning setting | DuelLab GameBench 2 | 45.0 | 94.0† |
| Design Arena Website | Design Arena | 1231† | 1333† |
| FrontierCode 1.1 (AA) | Artificial Analysis | 38.8%† | 47.0%† |
| FrontierCode 2.0 (AA) | Artificial Analysis | 17.1%† | 42.4%† |
| Agents & tools | |||
| Terminal-Bench | Terminal-Bench | – | 60.3%† |
| Remote Labor Index | Scale AI / CAIS | – | 20.8% |
| APEX-Agents | Mercor | 47.3%† | 64.7% |
| Arena Agent | Arena (LMArena) | – | 14.2† |
| LiveBench Long Agentic | LiveBench | – | 75.6% |
| LiveBench Browser | LiveBench | – | 91.3% |
| LiveBench Agentic Coding | LiveBench | 55.0%† | 57.3%† |
| Terminal-Bench 2.1 (Vals) | Vals AI | 50.2%† | 87.3%† |
| HiL-Bench | Scale AI SEAL | 38.5%† | 65.3% |
| GDPval-AA v2.1, normalised score | Artificial Analysis | 47.5%† | 53.7%† |
| GDPval-AA v2.1 | Artificial Analysis | 1457† | 1584† |
| τ³-Banking (AA) | Artificial Analysis | 39.6%† | 41.4%† |
| ITBench SRE (AA) | Artificial Analysis | – | 48.6%† |
| Analyst Agent (AA) | Artificial Analysis | – | 51.3%† |
| CUA-bench | Vals AI | – | 19.2%† |
| CyberBench | Vals AI | 72.9%† | 41.1%† |
| Finance Agent v2 | Vals AI | 50.4%† | 53.5%† |
| Finance Agent v2.1 | Vals AI | 57.1%† | 60.4%† |
| Harvey's Legal Agent Benchmark v1.1 | Vals AI | 10.8%† | 7.1%† |
| Harvey's Legal Agent Benchmark v1 | Vals AI | 7.5%† | 5.4%† |
| Legal Research Bench | Vals AI | 40.9%† | 39.4%† |
| Robo-bench | Vals AI | – | 23.3%† |
| SkillsBench | Vals AI | 53.8%† | – |
| SREBench | Vals AI | 1.9%† | 56.9%† |
| Tax Agent Bench | Vals AI | 26.8%† | 20.7%† |
| Terminal-Bench 2.0 (Vals) | Vals AI | 56.2%† | – |
| Terminal-Bench 4.0 (Vals) | Vals AI | 14.1%† | 59.6%† |
| Terminal-Bench Science | Vals AI | 4.3%† | 62.9%† |
| Time Horizon Index: KSP | Vals AI | – | 90.5%† |
| Arena Search | Arena (LMArena) | – | 1245† |
| BALROG | BALROG | – | 68.3%† |
| Vending-Bench 2 | Andon Labs | 3284.5† | 15514.7 |
| AutomationBench | Artificial Analysis | 56.7%† | 68.5%† |
| GDP.pdf (AA) | Artificial Analysis | 11.4%† | 31.0%† |
| Harvey LAB-AA v1.1 | Harvey | – | 8.6%† |
| EnterpriseOps-Gym | Artificial Analysis | 49.6%† | – |
| AA-Briefcase v1.1 | Artificial Analysis | 1253† | 1569† |
| GDPx | GDPx by SemiAnalysis | – | 9.6%† |
| Design Arena Agentic | Design Arena | 921† | 1393† |
| Terminal-Bench-Science 0.1 (AA) | Artificial Analysis | 5.7%† | 63.3%† |
| CWE-Bench (AA) | Artificial Analysis | – | 63.3%† |
| DeepSec-Bench (AA) | Artificial Analysis | – | 36.9%† |
| CyberGym end-to-end (AA) | Artificial Analysis | – | 0.0%† |
| FACTS Search | – | 93.3% | |
| Maths | |||
| FrontierMath Tiers 1–3 | Epoch AI Benchmarking Hub | 64.6%† | 93.7%† |
| FrontierMath Tier 4 | Epoch AI Benchmarking Hub | 26.8%† | 97.6%† |
| OTIS Mock AIME | Epoch AI Benchmarking Hub | 95.6% | 100.0%† |
| ProofBench | Vals AI | 50.0%† | 99.0% |
| LiveBench Mathematics | LiveBench | 95.1%† | 96.8%† |
| AIME 2026 | MathArena | 96.7%† | – |
| HMMT February 2026 | MathArena | 93.9%† | – |
| USAMO 2026 | MathArena | 60.7%† | – |
| MathArena Apex | MathArena | 28.1%† | – |
| Arena Maths | Arena (LMArena) | 1465 | 1481† |
| Knowledge | |||
| SimpleQA Verified | Epoch AI Benchmarking Hub | 52.9%† | 75.6%† |
| LiveBench 2.0 Data Analysis | LiveBench | – | 83.0% |
| LiveBench Research | LiveBench | – | 90.6% |
| LiveBench 1.0 Data Analysis | LiveBench | 79.2%† | 83.0%† |
| AA-Omniscience v1.1 | Artificial Analysis | -6.4† | 42.4† |
| MMLU-Pro | Vals AI | 87.3%† | – |
| LegalBench | Vals AI | 82.4%† | – |
| CorpFin | Vals AI | 65.4%† | – |
| TaxEval | Vals AI | 73.1%† | – |
| PRBench Finance | Scale AI SEAL | – | 47.5% |
| PRBench Legal | Scale AI SEAL | – | 48.4% |
| BioMysteryBench | Vals AI | – | 79.3%† |
| CaseLaw v2 | Vals AI | 59.4%† | – |
| Excel Modeling Benchmark | Vals AI | 52.8%† | 71.7%† |
| Excel Modeling Benchmark v1.1 | Vals AI | 65.1%† | 72.7%† |
| MedCode | Vals AI | 42.5%† | 48.5%† |
| MedScribe | Vals AI | 80.2%† | 87.9%† |
| Public Benefits Bench v1.1 | Vals AI | 62.9%† | – |
| DrugDiscoveryBench | Scale AI SEAL | 31.7%† | 68.7%† |
| EBR-bench | Epoch AI Benchmarking Hub | – | 76.2%† |
| GDP.pdf | Epoch AI Benchmarking Hub | – | 34.2%† |
| AA-Omniscience v1.1: accuracy | Artificial Analysis | 45.2%† | 61.6%† |
| AA-Omniscience v1.1: non-hallucination | Artificial Analysis | 5.9%† | 50.2%† |
| HealthBench Professional (AA) | OpenAI | – | 66.0%† |
| Instruction following | |||
| LiveBench Language | LiveBench | 82.1%† | 89.4%† |
| LiveBench Instruction Following | LiveBench | 67.7%† | 75.6%† |
| Arena Instruction Following | Arena (LMArena) | 1461 | 1473† |
| Human preference | |||
| Arena Text | Arena (LMArena) | 1464 | 1475† |
| EQ-Bench 4 | EQ-Bench | 1156† | – |
| Arena Creative Writing | Arena (LMArena) | 1444 | 1450† |
| Arena Multi-turn | Arena (LMArena) | 1469 | 1483† |
| Arena Longer Queries | Arena (LMArena) | 1474 | 1486† |
| Multimodal | |||
| Arena Vision | Arena (LMArena) | – | 1279† |
| LiveBench Design | LiveBench | – | 80.0% |
| MMMU-Pro | Artificial Analysis | – | 86.9%† |
| Blueprint-Bench 2 | Epoch AI Benchmarking Hub | – | 49.7% |
| Code2Video-AA | Artificial Analysis | – | 1427† |
| AA-3D | Artificial Analysis | – | 1168† |
| Long context | |||
| AA-LCR v1.1 | Artificial Analysis | 80.3%† | 80.7%† |
| Arena Document | Arena (LMArena) | – | 1473† |
| MLCR | Artificial Analysis | – | 41.7%† |
| Composite | |||
| Epoch Capabilities Index | Epoch AI Benchmarking Hub | 155.9† | 169.1 |
| LiveBench 2.0 | LiveBench | – | 84.5% |
| LiveBench 1.0 | LiveBench | 77.4%† | 82.2%† |
| AA Intelligence Index v5.0 | Artificial Analysis | 23.1† | 56.9† |
| AA Intelligence Index v4.3.2 | Artificial Analysis | 36† | 52.7† |
| AA Progress Index v1 | Artificial Analysis | 311.5† | 349.6† |
| Vals Index | Vals AI | 53.0† | 65.0† |
| Vals Finance Index | Vals AI | 52.9%† | 55.6%† |
| Vals RSI Index | Vals AI | – | 27.1%† |
| Vals Science Index | Vals AI | – | 65.1%† |
| Vals Router Index | Vals AI | 53.0%† | 65.0% |
| AA Cyber Index | Artificial Analysis | – | 33.4† |
| Safety & honesty | |||
| FORTRESS | Scale AI SEAL | – | 12.1% |
| Metadata | |||
| AA Openness Index | Artificial Analysis | 44.4† | – |
Data as of 2026-11-09. Best configuration of each model; every score links to its source on the model pages.
DuelLab GameBench 2, release gb2-20261103-eaea31df94e6 (https://benchmarks.duellab.org/releases/gb2-20261103-eaea31df94e6/), CC BY 4.0 (https://creativecommons.org/licenses/by/4.0/). Scores mapped to BenchLeader model names; not endorsed by DuelLab.
What changed
- DeepSeek V4 Pro (high): BenchLeader Index 57.5 → 59.1.
- GPT-6 Astra: 5 score revisions.
- GPT-6 Astra: 1 score revision.
- DeepSeek V4 Pro (high): 1 score revision.
- GPT-6 Astra: 1 new result (FORTRESS); 1 score revision.
- DeepSeek V4 Pro (high): 1 score revision.
- GPT-6 Astra: 1 score revision.
- GPT-6 Astra: 3 score revisions.
- GPT-6 Astra: 1 new result (Vals Router Index).
- DeepSeek V4 Pro (high): 16 new results (GPQA Diamond, OTIS Mock AIME, SciCode…); input price moved from $0.44/M to $0.66/M (50% up); output price moved from $0.87/M to $1.98/M (128% up).
Related comparisons
- DeepSeek V4 Pro vs Claude Fable 5.2
- DeepSeek V4 Pro vs Claude Opus 5.5
- DeepSeek V4 Pro vs Gemini 4 Carbon
- GPT-6 Astra vs Claude Fable 5.2
- GPT-6 Astra vs Claude Opus 5.5
- GPT-6 Astra vs Gemini 4 Carbon
- GPT-6 Astra vs GPT-6.1 Sol
- DeepSeek V4 Pro vs GPT-5.6 Sol
Rankings: Best LLM overall · Best LLM for agents · Best cheap LLMs
DeepSeek V4 Pro vs GPT-6 Astra: questions
- Is GPT-6 Astra better than DeepSeek V4 Pro?
- GPT-6 Astra leads on quality: 68.1 vs 59.1. The BenchLeader Index combines every independent quality benchmark; GPT-6 Astra is ahead overall as of 9 Nov 2026, but check the category scores for your use.
- DeepSeek V4 Pro vs GPT-6 Astra: which is cheaper, and will that change?
- DeepSeek V4 Pro is cheaper today: $0.990 against $20.00 per million tokens, blended at three input tokens per output token. No price change has been announced for either.
- Is DeepSeek V4 Pro or GPT-6 Astra better for coding?
- GPT-6 Astra scores higher in coding (83 vs 54 on the category index, where 50 is average).
- Which is faster, DeepSeek V4 Pro or GPT-6 Astra?
- DeepSeek V4 Pro streams faster (44 vs 27 output tokens per second, Artificial Analysis 7-day median).
- Which is better for long documents?
- Both take about 1.1M tokens: DeepSeek V4 Pro 1,048,576, GPT-6 Astra 1,050,000.
More questions (2)
- Which has lower latency?
- DeepSeek V4 Pro answers first: 1.5 s against 4.8 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.
- When was GPT-6 Astra released?
- GPT-6 Astra was released on 2 Sep 2026.