Model comparison
DeepSeek-V3.2-Exp vs GPT-6 Sol
GPT-6 Sol is the stronger model overall, scoring 49.2 to 44.9 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 . 17 shared benchmarks.
DeepSeekSummary
- They share 17 benchmarks with published results for both. DeepSeek-V3.2-Exp scores higher in 6 categories and GPT-6 Sol in 3 categories; 8 gaps are clear of the uncertainty.
- The widest gap is in reasoning, where GPT-6 Sol leads 49.3 to 22.0.
- The biggest single-benchmark swing is NYT Connections (extended): 36.7% for DeepSeek-V3.2-Exp and 90.1% 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 | 44.9 | 49.2 |
| 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 | 48 | 22 |
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Category by category
Coding GPT-6 Sol leads
DeepSeek-V3.2-Exp: 46.4 (#61), GPT-6 Sol: 59.6 (#11)
| Benchmark | DeepSeek-V3.2-Exp | GPT-6 Sol |
|---|---|---|
| LMArena WebDev | 1362 | 1692 |
| LMArena Coding | 1454 | 1453 |
| DeepSWE | — | 68.8% |
| FrontierCode | — | 49.3% |
| SWE-bench Verified (bash only) | 70% | — |
| Aider Polyglot | 74.2% | — |
| SWE-bench Multilingual | 59% | — |
| SciCode | 38.9% | — |
| WeirdML | 39.5% | — |
Agentic & Tool Use DeepSeek-V3.2-Exp leads
DeepSeek-V3.2-Exp: 37.9 (#31), GPT-6 Sol: 33.5 (#49)
| Benchmark | DeepSeek-V3.2-Exp | GPT-6 Sol |
|---|---|---|
| Terminal-Bench | 39.6% | — |
| Berkeley Function Calling Leaderboard | 56.7% | — |
| TheAgentCompany | 42.9% | — |
| GDP.pdf | — | 28% |
| Vending-Bench 2 | 1,034 | — |
Reasoning GPT-6 Sol leads
DeepSeek-V3.2-Exp: 22.0 (#185), GPT-6 Sol: 49.3 (#34)
| Benchmark | DeepSeek-V3.2-Exp | GPT-6 Sol |
|---|---|---|
| NYT Connections (extended) | 36.7% | 90.1% |
| LMArena Hard Prompts | 1433 | 1420 |
| ARC-AGI-2 | 4% | — |
| Kagi LLM Benchmark | 52.2% | — |
| ARC-AGI-1 | 57% | — |
| CritPt | 2.9% | — |
| Chess Puzzles | 14% | — |
| Thematic Generalization | 65% | — |
| EBR-Bench | — | 53.3% |
| DTBench | 87.7% | — |
| LMCA | 29.1% | — |
| Epoch Capabilities Index | 146.28 | — |
Math DeepSeek-V3.2-Exp leads
DeepSeek-V3.2-Exp: 40.8 (#75), GPT-6 Sol: 35.8 (#135)
| Benchmark | DeepSeek-V3.2-Exp | GPT-6 Sol |
|---|---|---|
| LMArena Math | 1435 | 1404 |
| MathArena Final-Answer Competitions | 57.7% | — |
| OTIS Mock AIME 2024-2025 | 87.8% | — |
| ProofBench | 8% | — |
| FrontierMath (Feb 2025 set) | 22.1% | — |
| FrontierMath Tier 4 (v1) | 2.1% | — |
Knowledge DeepSeek-V3.2-Exp leads
DeepSeek-V3.2-Exp: 51.6 (#61), GPT-6 Sol: 43.0 (#87)
| Benchmark | DeepSeek-V3.2-Exp | GPT-6 Sol |
|---|---|---|
| Vectara Hallucination Rate | 5.3% | 6.5% |
| LMArena Expert | 1436 | 1449 |
| GPQA Diamond | 83.4% | — |
Multimodal Not comparable
DeepSeek-V3.2-Exp: —, GPT-6 Sol: 47.6 (#9)
| Benchmark | DeepSeek-V3.2-Exp | GPT-6 Sol |
|---|---|---|
| Furniture Assembly | — | 58.3% |
Multilingual DeepSeek-V3.2-Exp leads
DeepSeek-V3.2-Exp: 52.4 (#85), GPT-6 Sol: 51.0 (#113)
| Benchmark | DeepSeek-V3.2-Exp | GPT-6 Sol |
|---|---|---|
| LMArena Non-English | 1409 | 1389 |
| LMArena Chinese | 1461 | 1416 |
| LMArena Russian | 1423 | 1407 |
| LMArena Spanish | 1441 | 1377 |
| LMArena French | 1435 | — |
| LMArena German | 1440 | — |
| LMArena Japanese | 1377 | — |
| LMArena Korean | 1372 | — |
Instruction Following Too close to call
DeepSeek-V3.2-Exp: 74.7 (#89), GPT-6 Sol: 74.4 (#97)
| Benchmark | DeepSeek-V3.2-Exp | GPT-6 Sol |
|---|---|---|
| LMArena Instruction Following | 1413 | 1406 |
Long Context DeepSeek-V3.2-Exp leads
DeepSeek-V3.2-Exp: 47.6 (#15), GPT-6 Sol: 43.2 (#100)
| Benchmark | DeepSeek-V3.2-Exp | GPT-6 Sol |
|---|---|---|
| LMArena Longer Query | 1428 | 1415 |
| Fiction.LiveBench | 83.3% | — |
| CL-bench | 13.2% | — |
| CL-bench Life | 9.5% | — |
Writing & Preference GPT-6 Sol leads
DeepSeek-V3.2-Exp: 62.5 (#73), GPT-6 Sol: 72.3 (#15)
| Benchmark | DeepSeek-V3.2-Exp | GPT-6 Sol |
|---|---|---|
| LMArena Text | 1425 | 1396 |
| LMArena Creative Writing | 1403 | 1392 |
| EQ-Bench Creative Writing | 1515 | 2125 |
| LMArena Multi-Turn | 1427 | 1405 |
Frequently asked questions
Is DeepSeek-V3.2-Exp better than GPT-6 Sol?
GPT-6 Sol is the stronger model overall, scoring 49.2 to 44.9 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: 59.6 versus 46.4 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?
17 benchmarks have published results for both models. DeepSeek-V3.2-Exp has 48 scored results on Noometry and GPT-6 Sol has 22.