Model comparison
DeepSeek-V3.2-Exp vs GPT-5.3 Chat
DeepSeek-V3.2-Exp is the stronger model overall, scoring 44.3 to 42.8 on the Noometry Index.
Last verified . 18 shared benchmarks.
DeepSeekSummary
- They share 18 benchmarks with published results for both. DeepSeek-V3.2-Exp scores higher in 6 categories and GPT-5.3 Chat in 2 categories; 7 gaps are clear of the uncertainty.
- The widest gap is in knowledge, where DeepSeek-V3.2-Exp leads 51.7 to 38.8.
- DeepSeek-V3.2-Exp is cheaper at $0.26 / $0.38 per million input/output tokens, against $1.75 / $14 for GPT-5.3 Chat.
- DeepSeek-V3.2-Exp accepts more context: 164K tokens versus 128K.
- DeepSeek-V3.2-Exp has downloadable open weights; the other is API-only.
Side by side
| DeepSeek-V3.2-Exp | GPT-5.3 Chat | |
|---|---|---|
| Provider | DeepSeek | OpenAI |
| Noometry Index | 44.3 | 42.8 |
| Released | 2025-09-29 | 2026-03-03 |
| Weights | Open | Proprietary |
| Context window | 164K | 128K |
| Max output | 66K | 16K |
| Input $ / M tokens | $0.26 | $1.75 |
| Output $ / M tokens | $0.38 | $14 |
| Results tracked | 49 | 18 |
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Category by category
Coding DeepSeek-V3.2-Exp leads
DeepSeek-V3.2-Exp: 46.5 (#65), GPT-5.3 Chat: 41.4 (#124)
| Benchmark | DeepSeek-V3.2-Exp | GPT-5.3 Chat |
|---|---|---|
| LMArena Coding | 1454 | 1408 |
| SWE-bench Verified (bash only) | 70% | — |
| Aider Polyglot | 74.2% | — |
| LMArena WebDev | 1362 | — |
| SWE-bench Multilingual | 59% | — |
| SciCode | 38.9% | — |
| WeirdML | 39.5% | — |
Agentic & Tool Use Not comparable
DeepSeek-V3.2-Exp: 32.7 (#59), GPT-5.3 Chat: —
| Benchmark | DeepSeek-V3.2-Exp | GPT-5.3 Chat |
|---|---|---|
| Terminal-Bench | 39.6% | — |
| APEX-Agents | 21.3% | — |
| Berkeley Function Calling Leaderboard | 56.7% | — |
| TheAgentCompany | 42.9% | — |
| Vending-Bench 2 | 1,034 | — |
Reasoning GPT-5.3 Chat leads
DeepSeek-V3.2-Exp: 22.1 (#208), GPT-5.3 Chat: 28.5 (#102)
| Benchmark | DeepSeek-V3.2-Exp | GPT-5.3 Chat |
|---|---|---|
| LMArena Hard Prompts | 1434 | 1399 |
| ARC-AGI-2 | 4% | — |
| Kagi LLM Benchmark | 52.2% | — |
| NYT Connections (extended) | 36.7% | — |
| ARC-AGI-1 | 57% | — |
| CritPt | 2.9% | — |
| Chess Puzzles | 14% | — |
| Thematic Generalization | 65% | — |
| DTBench | 87.7% | — |
| LMCA | 29.1% | — |
| Epoch Capabilities Index | 146.27 | — |
Math DeepSeek-V3.2-Exp leads
DeepSeek-V3.2-Exp: 41.7 (#87), GPT-5.3 Chat: 38.2 (#142)
| Benchmark | DeepSeek-V3.2-Exp | GPT-5.3 Chat |
|---|---|---|
| LMArena Math | 1435 | 1389 |
| 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: 51.7 (#66), GPT-5.3 Chat: 38.8 (#140)
| Benchmark | DeepSeek-V3.2-Exp | GPT-5.3 Chat |
|---|---|---|
| LMArena Expert | 1436 | 1397 |
| GPQA Diamond | 83.4% | — |
| Vectara Hallucination Rate | 5.3% | — |
Multilingual DeepSeek-V3.2-Exp leads
DeepSeek-V3.2-Exp: 52.2 (#90), GPT-5.3 Chat: 50.3 (#124)
| Benchmark | DeepSeek-V3.2-Exp | GPT-5.3 Chat |
|---|---|---|
| LMArena Non-English | 1409 | 1382 |
| LMArena Chinese | 1461 | 1432 |
| LMArena French | 1433 | 1397 |
| LMArena German | 1440 | 1384 |
| LMArena Japanese | 1374 | 1352 |
| LMArena Korean | 1371 | 1346 |
| LMArena Russian | 1424 | 1400 |
| LMArena Spanish | 1440 | 1371 |
Instruction Following DeepSeek-V3.2-Exp leads
DeepSeek-V3.2-Exp: 74.5 (#93), GPT-5.3 Chat: 72.8 (#129)
| Benchmark | DeepSeek-V3.2-Exp | GPT-5.3 Chat |
|---|---|---|
| LMArena Instruction Following | 1413 | 1378 |
Long Context DeepSeek-V3.2-Exp leads
DeepSeek-V3.2-Exp: 47.6 (#16), GPT-5.3 Chat: 42.6 (#120)
| Benchmark | DeepSeek-V3.2-Exp | GPT-5.3 Chat |
|---|---|---|
| LMArena Longer Query | 1428 | 1396 |
| Fiction.LiveBench | 83.3% | — |
| CL-bench | 13.2% | — |
| CL-bench Life | 9.5% | — |
Writing & Preference Too close to call
DeepSeek-V3.2-Exp: 62.4 (#77), GPT-5.3 Chat: 63.1 (#68)
| Benchmark | DeepSeek-V3.2-Exp | GPT-5.3 Chat |
|---|---|---|
| LMArena Text | 1425 | 1389 |
| LMArena Creative Writing | 1403 | 1355 |
| EQ-Bench Creative Writing | 1515 | 1690 |
| LMArena Multi-Turn | 1427 | 1412 |
Frequently asked questions
Is DeepSeek-V3.2-Exp better than GPT-5.3 Chat?
DeepSeek-V3.2-Exp is the stronger model overall, scoring 44.3 to 42.8 on the Noometry Index.
Which is cheaper, DeepSeek-V3.2-Exp or GPT-5.3 Chat?
DeepSeek-V3.2-Exp is cheaper. It lists at $0.26 per million input tokens and $0.38 per million output tokens; GPT-5.3 Chat lists at $1.75 and $14.
Is DeepSeek-V3.2-Exp or GPT-5.3 Chat better for coding?
DeepSeek-V3.2-Exp scores higher on coding benchmarks: 46.5 versus 41.4 in the Noometry coding category.
Which has the bigger context window?
DeepSeek-V3.2-Exp does, with 164K tokens against 128K.
How many benchmarks do DeepSeek-V3.2-Exp and GPT-5.3 Chat share?
18 benchmarks have published results for both models. DeepSeek-V3.2-Exp has 49 scored results on Noometry and GPT-5.3 Chat has 18.