Model comparison
DeepSeek-V3.2-Exp vs GPT-6 Astra
GPT-6 Astra is the stronger model overall, scoring 70.8 to 44.3 on the Noometry Index. DeepSeek-V3.2-Exp costs 69× less per token, which makes it the better buy when GPT-6 Astra's lead doesn't matter for your workload.
Last verified . 35 shared benchmarks.
DeepSeekSummary
- They share 35 benchmarks with published results for both. DeepSeek-V3.2-Exp scores higher in 1 category and GPT-6 Astra in 8 categories; 9 gaps are clear of the uncertainty.
- The widest gap is in reasoning, where GPT-6 Astra leads 85.1 to 22.1.
- The biggest single-benchmark swing is ProofBench: 8% for DeepSeek-V3.2-Exp and 99% for GPT-6 Astra.
- DeepSeek-V3.2-Exp is cheaper at $0.26 / $0.38 per million input/output tokens, against $10 / $50 for GPT-6 Astra.
- GPT-6 Astra accepts more context: 1.05M tokens versus 164K.
- DeepSeek-V3.2-Exp has downloadable open weights; the other is API-only.
Side by side
| DeepSeek-V3.2-Exp | GPT-6 Astra | |
|---|---|---|
| Provider | DeepSeek | OpenAI |
| Noometry Index | 44.3 | 70.8 |
| Released | 2025-09-29 | 2026-09-03 |
| Weights | Open | Proprietary |
| Context window | 164K | 1.05M |
| Max output | 66K | 128K |
| Input $ / M tokens | $0.26 | $10 |
| Output $ / M tokens | $0.38 | $50 |
| Results tracked | 49 | 56 |
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Category by category
Coding GPT-6 Astra leads
DeepSeek-V3.2-Exp: 46.5 (#65), GPT-6 Astra: 73.7 (#2)
| Benchmark | DeepSeek-V3.2-Exp | GPT-6 Astra |
|---|---|---|
| LMArena WebDev | 1362 | 1786 |
| SciCode | 38.9% | 56.5% |
| WeirdML | 39.5% | 93.6% |
| LMArena Coding | 1454 | 1487 |
| DeepSWE | — | 74.1% |
| FrontierCode | — | 53.3% |
| SWE-bench Verified (bash only) | 70% | — |
| Aider Polyglot | 74.2% | — |
| SWE-bench Multilingual | 59% | — |
| FrontierSWE | — | 65.5% |
| GSO | — | 79.4% |
| MirrorCode | — | 46.7% |
| ALE-Bench | — | 2,951 |
Agentic & Tool Use GPT-6 Astra leads
DeepSeek-V3.2-Exp: 32.7 (#59), GPT-6 Astra: 52.9 (#3)
| Benchmark | DeepSeek-V3.2-Exp | GPT-6 Astra |
|---|---|---|
| APEX-Agents | 21.3% | 64.7% |
| Vending-Bench 2 | 1,034 | 15,515 |
| Terminal-Bench | 39.6% | — |
| Berkeley Function Calling Leaderboard | 56.7% | — |
| Remote Labor Index | — | 20.8% |
| TheAgentCompany | 42.9% | — |
| BALROG | — | 68.3% |
| GDP.pdf | — | 34.2% |
Reasoning GPT-6 Astra leads
DeepSeek-V3.2-Exp: 22.1 (#208), GPT-6 Astra: 85.1 (#1)
| Benchmark | DeepSeek-V3.2-Exp | GPT-6 Astra |
|---|---|---|
| ARC-AGI-2 | 4% | 95% |
| NYT Connections (extended) | 36.7% | 98.1% |
| ARC-AGI-1 | 57% | 98.5% |
| CritPt | 2.9% | 31.7% |
| Chess Puzzles | 14% | 72% |
| LMArena Hard Prompts | 1434 | 1462 |
| DTBench | 87.7% | 97.3% |
| LMCA | 29.1% | 64.4% |
| Epoch Capabilities Index | 146.27 | 166.45 |
| Kagi LLM Benchmark | 52.2% | — |
| Thematic Generalization | 65% | — |
| EBR-Bench | — | 76.2% |
| Mystery Game Puzzles | — | 84% |
| Bench to the Future 3 | — | 0.14 |
Math GPT-6 Astra leads
DeepSeek-V3.2-Exp: 41.7 (#87), GPT-6 Astra: 93.5 (#2)
| Benchmark | DeepSeek-V3.2-Exp | GPT-6 Astra |
|---|---|---|
| OTIS Mock AIME 2024-2025 | 87.8% | 100% |
| ProofBench | 8% | 99% |
| LMArena Math | 1435 | 1465 |
| FrontierMath (Tiers 1-3) | — | 93.7% |
| FrontierMath Tier 4 | — | 97.6% |
| MathArena Final-Answer Competitions | 57.7% | — |
| FrontierMath (Feb 2025 set) | 22.1% | — |
| FrontierMath Erdős | — | 2.9% |
| FrontierMath Tier 4 (v1) | 2.1% | — |
Knowledge GPT-6 Astra leads
DeepSeek-V3.2-Exp: 51.7 (#66), GPT-6 Astra: 75.3 (#1)
| Benchmark | DeepSeek-V3.2-Exp | GPT-6 Astra |
|---|---|---|
| GPQA Diamond | 83.4% | 95.8% |
| Vectara Hallucination Rate | 5.3% | 8.7% |
| LMArena Expert | 1436 | 1483 |
| Humanity's Last Exam | — | 54.8% |
| SimpleQA Verified | — | 75.6% |
Multimodal Not comparable
DeepSeek-V3.2-Exp: —, GPT-6 Astra: 55.0 (#3)
| Benchmark | DeepSeek-V3.2-Exp | GPT-6 Astra |
|---|---|---|
| LMArena Vision | — | 1281 |
| Blueprint-Bench 2 | — | 49.7% |
| Furniture Assembly | — | 80% |
| LMArena Document | — | 1468 |
Multilingual GPT-6 Astra leads
DeepSeek-V3.2-Exp: 52.2 (#90), GPT-6 Astra: 53.7 (#61)
| Benchmark | DeepSeek-V3.2-Exp | GPT-6 Astra |
|---|---|---|
| LMArena Non-English | 1409 | 1430 |
| LMArena Chinese | 1461 | 1484 |
| LMArena French | 1433 | 1456 |
| LMArena German | 1440 | 1440 |
| LMArena Japanese | 1374 | 1379 |
| LMArena Korean | 1371 | 1426 |
| LMArena Russian | 1424 | 1436 |
| LMArena Spanish | 1440 | 1407 |
Instruction Following GPT-6 Astra leads
DeepSeek-V3.2-Exp: 74.5 (#93), GPT-6 Astra: 76.3 (#44)
| Benchmark | DeepSeek-V3.2-Exp | GPT-6 Astra |
|---|---|---|
| LMArena Instruction Following | 1413 | 1450 |
Long Context DeepSeek-V3.2-Exp leads
DeepSeek-V3.2-Exp: 47.6 (#16), GPT-6 Astra: 44.5 (#62)
| Benchmark | DeepSeek-V3.2-Exp | GPT-6 Astra |
|---|---|---|
| LMArena Longer Query | 1428 | 1456 |
| Fiction.LiveBench | 83.3% | — |
| CL-bench | 13.2% | — |
| CL-bench Life | 9.5% | — |
Writing & Preference GPT-6 Astra leads
DeepSeek-V3.2-Exp: 62.4 (#77), GPT-6 Astra: 75.3 (#7)
| Benchmark | DeepSeek-V3.2-Exp | GPT-6 Astra |
|---|---|---|
| LMArena Text | 1425 | 1441 |
| LMArena Creative Writing | 1403 | 1418 |
| EQ-Bench Creative Writing | 1515 | 2173 |
| LMArena Multi-Turn | 1427 | 1448 |
Frequently asked questions
Is DeepSeek-V3.2-Exp better than GPT-6 Astra?
GPT-6 Astra is the stronger model overall, scoring 70.8 to 44.3 on the Noometry Index. DeepSeek-V3.2-Exp costs 69× 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-V3.2-Exp or GPT-6 Astra?
DeepSeek-V3.2-Exp is cheaper. It lists at $0.26 per million input tokens and $0.38 per million output tokens; GPT-6 Astra lists at $10 and $50.
Is DeepSeek-V3.2-Exp or GPT-6 Astra better for coding?
GPT-6 Astra scores higher on coding benchmarks: 73.7 versus 46.5 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-V3.2-Exp and GPT-6 Astra share?
35 benchmarks have published results for both models. DeepSeek-V3.2-Exp has 49 scored results on Noometry and GPT-6 Astra has 56.