Model comparison
DeepSeek-V3.2-Exp vs GPT-6 Sol
GPT-6 Sol is the stronger model overall, scoring 61.8 to 44.3 on the Noometry Index. DeepSeek-V3.2-Exp costs 14× less per token, which makes it the better buy when GPT-6 Sol's lead doesn't matter for your workload.
Last verified . 33 shared benchmarks.
DeepSeekSummary
- They share 33 benchmarks with published results for both. DeepSeek-V3.2-Exp scores higher in 3 categories and GPT-6 Sol in 6 categories; 8 gaps are clear of the uncertainty.
- The widest gap is in reasoning, where GPT-6 Sol leads 74.0 to 22.1.
- The biggest single-benchmark swing is ARC-AGI-2: 4% for DeepSeek-V3.2-Exp and 89.6% for GPT-6 Sol.
- DeepSeek-V3.2-Exp is cheaper at $0.26 / $0.38 per million input/output tokens, against $2 / $10 for GPT-6 Sol.
- GPT-6 Sol 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 Sol | |
|---|---|---|
| Provider | DeepSeek | OpenAI |
| Noometry Index | 44.3 | 61.8 |
| Released | 2025-09-29 | 2026-09-22 |
| Weights | Open | Proprietary |
| Context window | 164K | 1.05M |
| Max output | 66K | 128K |
| Input $ / M tokens | $0.26 | $2 |
| Output $ / M tokens | $0.38 | $10 |
| Results tracked | 49 | 45 |
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Category by category
Coding GPT-6 Sol leads
DeepSeek-V3.2-Exp: 46.5 (#65), GPT-6 Sol: 60.1 (#11)
| Benchmark | DeepSeek-V3.2-Exp | GPT-6 Sol |
|---|---|---|
| LMArena WebDev | 1362 | 1688 |
| SciCode | 38.9% | 57.6% |
| LMArena Coding | 1454 | 1447 |
| DeepSWE | — | 68.8% |
| FrontierCode | — | 49.3% |
| SWE-bench Verified (bash only) | 70% | — |
| Aider Polyglot | 74.2% | — |
| SWE-bench Multilingual | 59% | — |
| WeirdML | 39.5% | — |
| ALE-Bench | — | 2,462 |
Agentic & Tool Use GPT-6 Sol leads
DeepSeek-V3.2-Exp: 32.7 (#59), GPT-6 Sol: 37.2 (#36)
| Benchmark | DeepSeek-V3.2-Exp | GPT-6 Sol |
|---|---|---|
| APEX-Agents | 21.3% | 54.3% |
| Vending-Bench 2 | 1,034 | 14,428 |
| Terminal-Bench | 39.6% | — |
| Berkeley Function Calling Leaderboard | 56.7% | — |
| TheAgentCompany | 42.9% | — |
| GDP.pdf | — | 26.4% |
Reasoning GPT-6 Sol leads
DeepSeek-V3.2-Exp: 22.1 (#208), GPT-6 Sol: 74.0 (#9)
| Benchmark | DeepSeek-V3.2-Exp | GPT-6 Sol |
|---|---|---|
| ARC-AGI-2 | 4% | 89.6% |
| NYT Connections (extended) | 36.7% | 90.1% |
| ARC-AGI-1 | 57% | 95.5% |
| CritPt | 2.9% | 30.9% |
| LMArena Hard Prompts | 1434 | 1418 |
| DTBench | 87.7% | 97.3% |
| LMCA | 29.1% | 59.1% |
| Epoch Capabilities Index | 146.27 | 162.72 |
| Kagi LLM Benchmark | 52.2% | — |
| Chess Puzzles | 14% | — |
| Thematic Generalization | 65% | — |
| EBR-Bench | — | 53.3% |
| Mystery Game Puzzles | — | 56% |
Math GPT-6 Sol leads
DeepSeek-V3.2-Exp: 41.7 (#87), GPT-6 Sol: 87.2 (#7)
| Benchmark | DeepSeek-V3.2-Exp | GPT-6 Sol |
|---|---|---|
| OTIS Mock AIME 2024-2025 | 87.8% | 100% |
| ProofBench | 8% | 83% |
| LMArena Math | 1435 | 1402 |
| FrontierMath (Tiers 1-3) | — | 89.8% |
| FrontierMath Tier 4 | — | 90% |
| MathArena Final-Answer Competitions | 57.7% | — |
| FrontierMath (Feb 2025 set) | 22.1% | — |
| FrontierMath Tier 4 (v1) | 2.1% | — |
Knowledge GPT-6 Sol leads
DeepSeek-V3.2-Exp: 51.7 (#66), GPT-6 Sol: 64.8 (#15)
| Benchmark | DeepSeek-V3.2-Exp | GPT-6 Sol |
|---|---|---|
| GPQA Diamond | 83.4% | 94.3% |
| Vectara Hallucination Rate | 5.3% | 6.5% |
| LMArena Expert | 1436 | 1439 |
| SimpleQA Verified | — | 60.7% |
Multimodal Not comparable
DeepSeek-V3.2-Exp: —, GPT-6 Sol: 47.6 (#10)
| Benchmark | DeepSeek-V3.2-Exp | GPT-6 Sol |
|---|---|---|
| LMArena Vision | — | 1245 |
| Blueprint-Bench 2 | — | 36.9% |
| Furniture Assembly | — | 58.3% |
Multilingual DeepSeek-V3.2-Exp leads
DeepSeek-V3.2-Exp: 52.2 (#90), GPT-6 Sol: 50.5 (#118)
| Benchmark | DeepSeek-V3.2-Exp | GPT-6 Sol |
|---|---|---|
| LMArena Non-English | 1409 | 1385 |
| LMArena Chinese | 1461 | 1405 |
| LMArena French | 1433 | 1410 |
| LMArena German | 1440 | 1390 |
| LMArena Japanese | 1374 | 1385 |
| LMArena Korean | 1371 | 1341 |
| LMArena Russian | 1424 | 1401 |
| LMArena Spanish | 1440 | 1384 |
Instruction Following Too close to call
DeepSeek-V3.2-Exp: 74.5 (#93), GPT-6 Sol: 74.5 (#94)
| Benchmark | DeepSeek-V3.2-Exp | GPT-6 Sol |
|---|---|---|
| LMArena Instruction Following | 1413 | 1412 |
Long Context DeepSeek-V3.2-Exp leads
DeepSeek-V3.2-Exp: 47.6 (#16), GPT-6 Sol: 43.1 (#108)
| Benchmark | DeepSeek-V3.2-Exp | GPT-6 Sol |
|---|---|---|
| LMArena Longer Query | 1428 | 1411 |
| Fiction.LiveBench | 83.3% | — |
| CL-bench | 13.2% | — |
| CL-bench Life | 9.5% | — |
Writing & Preference GPT-6 Sol leads
DeepSeek-V3.2-Exp: 62.4 (#77), GPT-6 Sol: 71.9 (#18)
| Benchmark | DeepSeek-V3.2-Exp | GPT-6 Sol |
|---|---|---|
| LMArena Text | 1425 | 1395 |
| LMArena Creative Writing | 1403 | 1378 |
| EQ-Bench Creative Writing | 1515 | 2125 |
| LMArena Multi-Turn | 1427 | 1412 |
Frequently asked questions
Is DeepSeek-V3.2-Exp better than GPT-6 Sol?
GPT-6 Sol is the stronger model overall, scoring 61.8 to 44.3 on the Noometry Index. DeepSeek-V3.2-Exp costs 14× less per token, which makes it the better buy when GPT-6 Sol's lead doesn't matter for your workload.
Which is cheaper, DeepSeek-V3.2-Exp or GPT-6 Sol?
DeepSeek-V3.2-Exp is cheaper. It lists at $0.26 per million input tokens and $0.38 per million output tokens; GPT-6 Sol lists at $2 and $10.
Is DeepSeek-V3.2-Exp or GPT-6 Sol better for coding?
GPT-6 Sol scores higher on coding benchmarks: 60.1 versus 46.5 in the Noometry coding category.
Which has the bigger context window?
GPT-6 Sol does, with 1.05M tokens against 164K.
How many benchmarks do DeepSeek-V3.2-Exp and GPT-6 Sol share?
33 benchmarks have published results for both models. DeepSeek-V3.2-Exp has 49 scored results on Noometry and GPT-6 Sol has 45.