Model comparison
DeepSeek-V3.1 vs GPT-6 Astra
GPT-6 Astra is the stronger model overall, scoring 70.3 to 42.2 on the Noometry Index. DeepSeek-V3.1 costs 47× less per token, which makes it the better buy when GPT-6 Astra's lead doesn't matter for your workload.
Last verified . 20 shared benchmarks.
DeepSeekSummary
- They share 20 benchmarks with published results for both. DeepSeek-V3.1 scores higher in 0 categories and GPT-6 Astra in 8 categories; 8 gaps are clear of the uncertainty.
- The widest gap is in reasoning, where GPT-6 Astra leads 85.1 to 27.3.
- The biggest single-benchmark swing is WeirdML: 38.4% for DeepSeek-V3.1 and 93.6% for GPT-6 Astra.
- DeepSeek-V3.1 is cheaper at $0.25 / $0.95 per million input/output tokens, against $10 / $50 for GPT-6 Astra.
- GPT-6 Astra accepts more context: 1.05M tokens versus 164K.
- DeepSeek-V3.1 has downloadable open weights; the other is API-only.
Side by side
| DeepSeek-V3.1 | GPT-6 Astra | |
|---|---|---|
| Provider | DeepSeek | OpenAI |
| Noometry Index | 42.2 | 70.3 |
| Released | 2025-08-21 | 2026-09-03 |
| Weights | Open | Proprietary |
| Context window | 164K | 1.05M |
| Max output | 8K | 128K |
| Input $ / M tokens | $0.25 | $10 |
| Output $ / M tokens | $0.95 | $50 |
| Results tracked | 27 | 51 |
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Category by category
Coding GPT-6 Astra leads
DeepSeek-V3.1: 39.8 (#140), GPT-6 Astra: 70.5 (#3)
| Benchmark | DeepSeek-V3.1 | GPT-6 Astra |
|---|---|---|
| WeirdML | 38.4% | 93.6% |
| LMArena Coding | 1417 | 1488 |
| DeepSWE | — | 74.1% |
| FrontierCode | — | 53.3% |
| LMArena WebDev | — | 1792 |
| FrontierSWE | — | 65.5% |
| SciCode | — | 56.5% |
| MirrorCode | — | 46.7% |
| ALE-Bench | — | 2,951 |
Agentic & Tool Use Not comparable
DeepSeek-V3.1: —, GPT-6 Astra: 52.3 (#3)
| Benchmark | DeepSeek-V3.1 | GPT-6 Astra |
|---|---|---|
| APEX-Agents | — | 64.7% |
| Remote Labor Index | — | 20.8% |
| BALROG | — | 68.3% |
| Vending-Bench 2 | — | 15,515 |
Reasoning GPT-6 Astra leads
DeepSeek-V3.1: 27.3 (#109), GPT-6 Astra: 85.1 (#1)
| Benchmark | DeepSeek-V3.1 | GPT-6 Astra |
|---|---|---|
| LMArena Hard Prompts | 1417 | 1464 |
| DTBench | 82.7% | 97.3% |
| LMCA | 24.3% | 64.4% |
| Epoch Capabilities Index | 139.92 | 166.6 |
| ARC-AGI-2 | — | 95% |
| SimpleBench | 40% | — |
| Kagi LLM Benchmark | 53.2% | — |
| NYT Connections (extended) | — | 98.1% |
| ARC-AGI-1 | — | 98.5% |
| CritPt | — | 31.7% |
| Chess Puzzles | — | 72% |
| EBR-Bench | — | 76.2% |
| Mystery Game Puzzles | — | 84% |
| Bench to the Future 3 | — | 0.14 |
| ForecastBench | 58 | — |
Math GPT-6 Astra leads
DeepSeek-V3.1: 36.1 (#125), GPT-6 Astra: 93.4 (#1)
| Benchmark | DeepSeek-V3.1 | GPT-6 Astra |
|---|---|---|
| LMArena Math | 1420 | 1463 |
| FrontierMath (Tiers 1-3) | — | 93.7% |
| FrontierMath Tier 4 | — | 97.6% |
| OTIS Mock AIME 2024-2025 | — | 100% |
| ProofBench | — | 99% |
| FrontierMath Erdős | — | 2.9% |
Knowledge GPT-6 Astra leads
DeepSeek-V3.1: 43.0 (#86), GPT-6 Astra: 75.3 (#1)
| Benchmark | DeepSeek-V3.1 | GPT-6 Astra |
|---|---|---|
| Vectara Hallucination Rate | 5.5% | 8.7% |
| LMArena Expert | 1403 | 1486 |
| GPQA Diamond | — | 95.8% |
| Humanity's Last Exam | — | 54.8% |
| SimpleQA Verified | — | 75.6% |
Multimodal Not comparable
DeepSeek-V3.1: —, GPT-6 Astra: 55.1 (#3)
| Benchmark | DeepSeek-V3.1 | GPT-6 Astra |
|---|---|---|
| LMArena Vision | — | 1280 |
| Blueprint-Bench 2 | — | 49.7% |
| Furniture Assembly | — | 80% |
| LMArena Document | — | 1468 |
Multilingual GPT-6 Astra leads
DeepSeek-V3.1: 51.8 (#100), GPT-6 Astra: 54.0 (#53)
| Benchmark | DeepSeek-V3.1 | GPT-6 Astra |
|---|---|---|
| LMArena Non-English | 1400 | 1431 |
| LMArena Chinese | 1470 | 1495 |
| LMArena French | 1447 | 1467 |
| LMArena Russian | 1405 | 1434 |
| LMArena Spanish | 1429 | 1412 |
| LMArena German | 1412 | — |
| LMArena Japanese | 1374 | — |
| LMArena Korean | 1337 | — |
Instruction Following GPT-6 Astra leads
DeepSeek-V3.1: 74.1 (#105), GPT-6 Astra: 76.6 (#37)
| Benchmark | DeepSeek-V3.1 | GPT-6 Astra |
|---|---|---|
| LMArena Instruction Following | 1400 | 1454 |
Long Context GPT-6 Astra leads
DeepSeek-V3.1: 36.3 (#227), GPT-6 Astra: 44.7 (#57)
| Benchmark | DeepSeek-V3.1 | GPT-6 Astra |
|---|---|---|
| LMArena Longer Query | 1422 | 1460 |
| Fiction.LiveBench | 52.8% | — |
Writing & Preference GPT-6 Astra leads
DeepSeek-V3.1: 60.5 (#93), GPT-6 Astra: 75.7 (#7)
| Benchmark | DeepSeek-V3.1 | GPT-6 Astra |
|---|---|---|
| LMArena Text | 1420 | 1443 |
| LMArena Creative Writing | 1401 | 1423 |
| EQ-Bench Creative Writing | 1436 | 2173 |
| LMArena Multi-Turn | 1408 | 1457 |
Frequently asked questions
Is DeepSeek-V3.1 better than GPT-6 Astra?
GPT-6 Astra is the stronger model overall, scoring 70.3 to 42.2 on the Noometry Index. DeepSeek-V3.1 costs 47× less per token, which makes it the better buy when GPT-6 Astra's lead doesn't matter for your workload.
Which is cheaper, DeepSeek-V3.1 or GPT-6 Astra?
DeepSeek-V3.1 is cheaper. It lists at $0.25 per million input tokens and $0.95 per million output tokens; GPT-6 Astra lists at $10 and $50.
Is DeepSeek-V3.1 or GPT-6 Astra better for coding?
GPT-6 Astra scores higher on coding benchmarks: 70.5 versus 39.8 in the Noometry coding category.
Which has the bigger context window?
GPT-6 Astra does, with 1.05M tokens against 164K.
How many benchmarks do DeepSeek-V3.1 and GPT-6 Astra share?
20 benchmarks have published results for both models. DeepSeek-V3.1 has 27 scored results on Noometry and GPT-6 Astra has 51.