Model comparison
DeepSeek-V3.2-Exp vs GPT-5-Codex
DeepSeek-V3.2-Exp is the stronger model overall, scoring 44.3 to 37.9 on the Noometry Index.
Last verified . 3 shared benchmarks.
DeepSeekSummary
- They share 3 benchmarks with published results for both. DeepSeek-V3.2-Exp scores higher in 2 categories and GPT-5-Codex in 1 category; 3 gaps are clear of the uncertainty.
- The widest gap is in reasoning, where GPT-5-Codex leads 30.9 to 22.1.
- The biggest single-benchmark swing is Kagi LLM Benchmark: 52.2% for DeepSeek-V3.2-Exp and 70.3% for GPT-5-Codex.
- DeepSeek-V3.2-Exp is cheaper at $0.26 / $0.38 per million input/output tokens, against $1.25 / $10 for GPT-5-Codex.
- GPT-5-Codex accepts more context: 400K tokens versus 164K.
- DeepSeek-V3.2-Exp has downloadable open weights; the other is API-only.
Side by side
| DeepSeek-V3.2-Exp | GPT-5-Codex | |
|---|---|---|
| Provider | DeepSeek | OpenAI |
| Noometry Index | 44.3 | 37.9 |
| Released | 2025-09-29 | 2025-09-15 |
| Weights | Open | Proprietary |
| Context window | 164K | 400K |
| Max output | 66K | 128K |
| Input $ / M tokens | $0.26 | $1.25 |
| Output $ / M tokens | $0.38 | $10 |
| Results tracked | 49 | 3 |
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Category by category
Coding DeepSeek-V3.2-Exp leads
DeepSeek-V3.2-Exp: 46.5 (#65), GPT-5-Codex: 42.4 (#103)
| Benchmark | DeepSeek-V3.2-Exp | GPT-5-Codex |
|---|---|---|
| WeirdML | 39.5% | 54.5% |
| SWE-bench Verified (bash only) | 70% | — |
| Aider Polyglot | 74.2% | — |
| LMArena WebDev | 1362 | — |
| SWE-bench Multilingual | 59% | — |
| SciCode | 38.9% | — |
| LMArena Coding | 1454 | — |
Agentic & Tool Use DeepSeek-V3.2-Exp leads
DeepSeek-V3.2-Exp: 32.7 (#59), GPT-5-Codex: 31.0 (#72)
| Benchmark | DeepSeek-V3.2-Exp | GPT-5-Codex |
|---|---|---|
| Terminal-Bench | 39.6% | 44.3% |
| APEX-Agents | 21.3% | — |
| Berkeley Function Calling Leaderboard | 56.7% | — |
| TheAgentCompany | 42.9% | — |
| Vending-Bench 2 | 1,034 | — |
Reasoning GPT-5-Codex leads
DeepSeek-V3.2-Exp: 22.1 (#208), GPT-5-Codex: 30.9 (#83)
| Benchmark | DeepSeek-V3.2-Exp | GPT-5-Codex |
|---|---|---|
| Kagi LLM Benchmark | 52.2% | 70.3% |
| ARC-AGI-2 | 4% | — |
| NYT Connections (extended) | 36.7% | — |
| ARC-AGI-1 | 57% | — |
| CritPt | 2.9% | — |
| Chess Puzzles | 14% | — |
| Thematic Generalization | 65% | — |
| LMArena Hard Prompts | 1434 | — |
| DTBench | 87.7% | — |
| LMCA | 29.1% | — |
| Epoch Capabilities Index | 146.27 | — |
Math Not comparable
DeepSeek-V3.2-Exp: 41.7 (#87), GPT-5-Codex: —
| Benchmark | DeepSeek-V3.2-Exp | GPT-5-Codex |
|---|---|---|
| MathArena Final-Answer Competitions | 57.7% | — |
| OTIS Mock AIME 2024-2025 | 87.8% | — |
| ProofBench | 8% | — |
| LMArena Math | 1435 | — |
| FrontierMath (Feb 2025 set) | 22.1% | — |
| FrontierMath Tier 4 (v1) | 2.1% | — |
Knowledge Not comparable
DeepSeek-V3.2-Exp: 51.7 (#66), GPT-5-Codex: —
| Benchmark | DeepSeek-V3.2-Exp | GPT-5-Codex |
|---|---|---|
| GPQA Diamond | 83.4% | — |
| Vectara Hallucination Rate | 5.3% | — |
| LMArena Expert | 1436 | — |
Multilingual Not comparable
DeepSeek-V3.2-Exp: 52.2 (#90), GPT-5-Codex: —
| Benchmark | DeepSeek-V3.2-Exp | GPT-5-Codex |
|---|---|---|
| LMArena Non-English | 1409 | — |
| LMArena Chinese | 1461 | — |
| LMArena French | 1433 | — |
| LMArena German | 1440 | — |
| LMArena Japanese | 1374 | — |
| LMArena Korean | 1371 | — |
| LMArena Russian | 1424 | — |
| LMArena Spanish | 1440 | — |
Instruction Following Not comparable
DeepSeek-V3.2-Exp: 74.5 (#93), GPT-5-Codex: —
| Benchmark | DeepSeek-V3.2-Exp | GPT-5-Codex |
|---|---|---|
| LMArena Instruction Following | 1413 | — |
Long Context Not comparable
DeepSeek-V3.2-Exp: 47.6 (#16), GPT-5-Codex: —
| Benchmark | DeepSeek-V3.2-Exp | GPT-5-Codex |
|---|---|---|
| Fiction.LiveBench | 83.3% | — |
| CL-bench | 13.2% | — |
| CL-bench Life | 9.5% | — |
| LMArena Longer Query | 1428 | — |
Writing & Preference Not comparable
DeepSeek-V3.2-Exp: 62.4 (#77), GPT-5-Codex: —
| Benchmark | DeepSeek-V3.2-Exp | GPT-5-Codex |
|---|---|---|
| LMArena Text | 1425 | — |
| LMArena Creative Writing | 1403 | — |
| EQ-Bench Creative Writing | 1515 | — |
| LMArena Multi-Turn | 1427 | — |
Frequently asked questions
Is DeepSeek-V3.2-Exp better than GPT-5-Codex?
DeepSeek-V3.2-Exp is the stronger model overall, scoring 44.3 to 37.9 on the Noometry Index.
Which is cheaper, DeepSeek-V3.2-Exp or GPT-5-Codex?
DeepSeek-V3.2-Exp is cheaper. It lists at $0.26 per million input tokens and $0.38 per million output tokens; GPT-5-Codex lists at $1.25 and $10.
Is DeepSeek-V3.2-Exp or GPT-5-Codex better for coding?
DeepSeek-V3.2-Exp scores higher on coding benchmarks: 46.5 versus 42.4 in the Noometry coding category.
Which has the bigger context window?
GPT-5-Codex does, with 400K tokens against 164K.
How many benchmarks do DeepSeek-V3.2-Exp and GPT-5-Codex share?
3 benchmarks have published results for both models. DeepSeek-V3.2-Exp has 49 scored results on Noometry and GPT-5-Codex has 3.