DeepSeek V3.2 Exp vs GPT-5.2-Codex
GPT-5.2-Codex leads DeepSeek V3.2 Exp on the Artificial Analysis Intelligence Index, 28.5 to 16.6, and DeepSeek V3.2 Exp costs 34.1x less per output token ($0.41 vs $14.00 per million). Updated October 22, 2026; 9 benchmarks have a result for both.
DeepSeek V3.2 Exp or GPT-5.2-Codex: which to pick
Only GPT-5.2-Codex has a coding score, 45.4 on the Vibe Coding Index; DeepSeek V3.2 Exp has none, so the two cannot be ranked on coding.
Pick DeepSeek V3.2 Exp for output tokens 34.1x cheaper ($0.41 vs $14.00 per million).
- Shared results
- 9
- DeepSeek V3.2 Exp only
- 50
- GPT-5.2-Codex only
- 13
- Like-for-like categories
- 5 of 9
Only benchmarks that tested both models count toward a pick; a result only one model has is shown but not compared.
Which one for your work
Each card is decided by one number both models carry. Where only one has it, the card says so and makes no call.
Coding
Vibe Coding Index, six public coding benchmarks
No call
Only GPT-5.2-Codex has a coding score, so no call is made.
One side not measured
Intelligence
AA Intelligence Index
GPT-5.2-Codex
GPT-5.2-Codex leads, 28.5 against 16.6.
Same board, both measured
Web design
Design Arena: websites
No call
Only DeepSeek V3.2 Exp has a Design Arena: websites result, 1178.
Directional only
Long documents
Listed context window
GPT-5.2-Codex
GPT-5.2-Codex takes 400K tokens against 164K.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
DeepSeek V3.2 Exp
DeepSeek V3.2 Exp $0.0005, GPT-5.2-Codex $0.0088 per turn.
List prices
Cost per solved task
One SWE-bench Verified task, solved
No call
Only GPT-5.2-Codex has the number, $0.43.
Directional only
At a glance
- Coding index
- DeepSeek V3.2 Exp not benchmarked, GPT-5.2-Codex 45.4 from 3 benchmarks.
- Widest category
- Agentic work, 58.2 points apart on average over 1 shared benchmark.
- Widest single gap
- τ²-Bench Telecom: DeepSeek V3.2 Exp 33.9% against GPT-5.2-Codex 92.1%, 58.2 points apart on a 0 to 100 scale.
- Output price
- DeepSeek V3.2 Exp $0.41 against GPT-5.2-Codex $14.00 per million output tokens, 34.1x apart.
- Context window
- DeepSeek V3.2 Exp takes 164K tokens, GPT-5.2-Codex 400K.
Benchmark by benchmark
First the score: boards each model leads, per category, where both have a result. Then every board, a category to a fold; bars are each result on a 0 to 100 scale, so compare the two in a row, not one row with another.
| Category | DeepSeek V3.2 Exp | Bars | GPT-5.2-Codex | Ties |
|---|---|---|---|---|
| Coding | 0 | 3 | ||
| Intelligence | 0 | 1 | ||
| Agentic work | 0 | 1 | ||
| Reasoning | 0 | 2 | ||
| General | 0 | 2 | ||
| All shared boards | 0 | 9 |
CodingLike-for-likeSix of these boards feed the Vibe Coding Index03
| Benchmark | DeepSeek V3.2 Exp | GPT-5.2-Codex | Leader |
|---|---|---|---|
| Aider polyglot | 74.2% | Not measured | Only DeepSeek V3.2 Exp measured |
| SWE-bench Verified | Not measured | 72.8% | Only GPT-5.2-Codex measured |
| SWE-bench Multilingual | Not measured | 66.3% | Only GPT-5.2-Codex measured |
| LMArena WebDev | 1272 | 1339 | GPT-5.2-Codex |
| LiveCodeBench (Vals AI) | Not measured | 88.0% | Only GPT-5.2-Codex measured |
| SWE-bench Verified (Vals AI) | Not measured | 72.4% | Only GPT-5.2-Codex measured |
| Vibe Code Bench (Vals AI) | Not measured | 37.9% | Only GPT-5.2-Codex measured |
| LiveCodeBench (Artificial Analysis run) | 78.9% | Not measured | Only DeepSeek V3.2 Exp measured |
| Terminal-Bench Hard (Artificial Analysis run) | 31.1% | 37.1% | GPT-5.2-Codex |
| Terminal-Bench | Not measured | 66.5% | Only GPT-5.2-Codex measured |
| WeirdML | 39.5% | Not measured | Only DeepSeek V3.2 Exp measured |
| ALE-Bench | Not measured | 1299.9 | Only GPT-5.2-Codex measured |
| LMArena Text: coding | 1438 | Not measured | Only DeepSeek V3.2 Exp measured |
| LMArena Text: software and IT | 1438 | Not measured | Only DeepSeek V3.2 Exp measured |
| LMArena WebDev: React | Not measured | 1329 | Only GPT-5.2-Codex measured |
| LMArena WebDev: HTML | 1281 | 1348 | GPT-5.2-Codex |
| Neither model has a result on the other 23 boards here | |||
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index01
| Benchmark | DeepSeek V3.2 Exp | GPT-5.2-Codex | Leader |
|---|---|---|---|
| AA Intelligence Index | 16.6 | 28.5 | GPT-5.2-Codex |
| Epoch Capabilities Index | 144.9 | Not measured | Only DeepSeek V3.2 Exp measured |
Agentic workLike-for-likePublic boards, shown as published, kept out of the index01
| Benchmark | DeepSeek V3.2 Exp | GPT-5.2-Codex | Leader |
|---|---|---|---|
| Design Arena: agents building web apps | Not measured | 1051 | Only GPT-5.2-Codex measured |
| Design Arena: agents building full-stack apps | Not measured | 993 | Only GPT-5.2-Codex measured |
| Design Arena: agents building mobile apps | Not measured | 1090 | Only GPT-5.2-Codex measured |
| Design Arena: agents building Android apps | Not measured | 1176 | Only GPT-5.2-Codex measured |
| Design Arena: agents building Godot games | Not measured | 1142 | Only GPT-5.2-Codex measured |
| τ²-Bench Telecom | 33.9% | 92.1% | GPT-5.2-Codex |
| TheAgentCompany | 42.9% | Not measured | Only DeepSeek V3.2 Exp measured |
| Berkeley Function Calling Leaderboard (BFCL) | 56.7% | Not measured | Only DeepSeek V3.2 Exp measured |
| Neither model has a result on the other 33 boards here | |||
ReasoningLike-for-likePublic boards, shown as published, kept out of the index02
| Benchmark | DeepSeek V3.2 Exp | GPT-5.2-Codex | Leader |
|---|---|---|---|
| GPQA Diamond (Artificial Analysis run) | 79.7% | 89.9% | GPT-5.2-Codex |
| Humanity's Last Exam (Artificial Analysis run) | 14.9% | 35.7% | GPT-5.2-Codex |
| LMArena Text: hard prompts | 1429 | Not measured | Only DeepSeek V3.2 Exp measured |
| LMArena Text: expert | 1422 | Not measured | Only DeepSeek V3.2 Exp measured |
| LMArena Text: hard prompts in English | 1444 | Not measured | Only DeepSeek V3.2 Exp measured |
| Kagi LLM Benchmark | 52.2% | Not measured | Only DeepSeek V3.2 Exp measured |
| Neither model has a result on the other 16 boards here | |||
MathDirectional onlyPublic boards, shown as published, kept out of the index4 boards, none shared
| Benchmark | DeepSeek V3.2 Exp | GPT-5.2-Codex | Leader |
|---|---|---|---|
| AA Math Index | 87.7 | Not measured | Only DeepSeek V3.2 Exp measured |
| AIME 2025 (Artificial Analysis run) | 87.7% | Not measured | Only DeepSeek V3.2 Exp measured |
| LMArena Text: math | 1422 | Not measured | Only DeepSeek V3.2 Exp measured |
| LMArena Text: mathematical work | 1433 | Not measured | Only DeepSeek V3.2 Exp measured |
| Neither model has a result on the other 9 boards here | |||
KnowledgeDirectional onlyPublic boards, shown as published, kept out of the index5 boards, none shared
| Benchmark | DeepSeek V3.2 Exp | GPT-5.2-Codex | Leader |
|---|---|---|---|
| MMLU Pro (Artificial Analysis run) | 85.0% | Not measured | Only DeepSeek V3.2 Exp measured |
| LMArena Text: science | 1443 | Not measured | Only DeepSeek V3.2 Exp measured |
| LMArena Text: medicine and healthcare | 1468 | Not measured | Only DeepSeek V3.2 Exp measured |
| LMArena Text: legal and government | 1419 | Not measured | Only DeepSeek V3.2 Exp measured |
| LMArena Text: business and finance | 1412 | Not measured | Only DeepSeek V3.2 Exp measured |
| Neither model has a result on the other 13 boards here | |||
Web designDirectional onlyPublic boards, shown as published, kept out of the index7 boards, none shared
| Benchmark | DeepSeek V3.2 Exp | GPT-5.2-Codex | Leader |
|---|---|---|---|
| Design Arena: websites | 1178 | Not measured | Only DeepSeek V3.2 Exp measured |
| Design Arena: UI components | 1164 | Not measured | Only DeepSeek V3.2 Exp measured |
| Design Arena: game dev | 1144 | Not measured | Only DeepSeek V3.2 Exp measured |
| Design Arena: data viz | 1152 | Not measured | Only DeepSeek V3.2 Exp measured |
| Design Arena: 3D | 1148 | Not measured | Only DeepSeek V3.2 Exp measured |
| Design Arena: all code categories | 1170 | Not measured | Only DeepSeek V3.2 Exp measured |
| Design Arena: SVG | 1031 | Not measured | Only DeepSeek V3.2 Exp measured |
| Neither model has a result on the other 2 boards here | |||
GeneralLike-for-likePublic boards, shown as published, kept out of the index02
| Benchmark | DeepSeek V3.2 Exp | GPT-5.2-Codex | Leader |
|---|---|---|---|
| IFBench | 54.1% | 77.6% | GPT-5.2-Codex |
| Long context reasoning (AA-LCR) | 72.3% | 82.3% | GPT-5.2-Codex |
| DTBench | 87.7% | Not measured | Only DeepSeek V3.2 Exp measured |
| LMCA | 29.1% | Not measured | Only DeepSeek V3.2 Exp measured |
| Fiction.LiveBench | 83.3% | Not measured | Only DeepSeek V3.2 Exp measured |
| CL-bench | 13.2% | Not measured | Only DeepSeek V3.2 Exp measured |
| CL-bench Life | 9.5% | Not measured | Only DeepSeek V3.2 Exp measured |
| LMArena Text | 1425 | Not measured | Only DeepSeek V3.2 Exp measured |
| LMArena Text: instruction following | 1401 | Not measured | Only DeepSeek V3.2 Exp measured |
| LMArena Text: creative writing | 1404 | Not measured | Only DeepSeek V3.2 Exp measured |
| LMArena Text: English | 1441 | Not measured | Only DeepSeek V3.2 Exp measured |
| LMArena Text: non-English | 1404 | Not measured | Only DeepSeek V3.2 Exp measured |
| LMArena Text: Chinese | 1461 | Not measured | Only DeepSeek V3.2 Exp measured |
| LMArena Text: German | 1440 | Not measured | Only DeepSeek V3.2 Exp measured |
| LMArena Text: Japanese | 1331 | Not measured | Only DeepSeek V3.2 Exp measured |
| LMArena Text: Korean | 1372 | Not measured | Only DeepSeek V3.2 Exp measured |
| LMArena Text: French | 1434 | Not measured | Only DeepSeek V3.2 Exp measured |
| LMArena Text: Spanish | 1441 | Not measured | Only DeepSeek V3.2 Exp measured |
| LMArena Text: Russian | 1412 | Not measured | Only DeepSeek V3.2 Exp measured |
| LMArena Text: Polish | 1435 | Not measured | Only DeepSeek V3.2 Exp measured |
| LMArena Text: multi-turn | 1422 | Not measured | Only DeepSeek V3.2 Exp measured |
| LMArena Text: longer queries | 1418 | Not measured | Only DeepSeek V3.2 Exp measured |
| LMArena Text: writing and language | 1407 | Not measured | Only DeepSeek V3.2 Exp measured |
| LMArena Text: entertainment and media | 1407 | Not measured | Only DeepSeek V3.2 Exp measured |
| Neither model has a result on the other 22 boards here | |||
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | DeepSeek V3.2 Exp | GPT-5.2-Codex | Leader |
|---|---|---|---|
| Neither model has a result on 5 boards here | |||
What each workload costs
Three fixed token mixes at each model's listed price, so per-token rates become a bill.
Chat turn
1K input + 500 output tokens
DeepSeek V3.2 Exp costs less
Repository review
50K input + 3K output tokens
DeepSeek V3.2 Exp costs less
Agent loop
200K input + 10K output tokens
DeepSeek V3.2 Exp costs less
Listed OpenRouter rates, without cache or batch discounts. A workload that does not fit a model's context window would need splitting.
Specification differences
The leading value in a row is marked; a row with a missing value names no leader. Our two rows are marked in red.
| Specification | DeepSeek V3.2 Exp | OpenAI GPT-5.2-Codex |
|---|---|---|
| Maker | DeepSeek | OpenAI |
| Weights | Open weights | No public weights listed |
| Released | September 29, 2025 | January 14, 2026 |
| Context window | 164K | 400K |
| Input $/Mtok | $0.27 | $1.75 |
| Output $/Mtok | $0.41 | $14.00 |
| Coding index | Not benchmarked | 45.4 |
| Independent measurements behind it | 1 | 3 |
| Cost per solved task | Not computable | $0.43 |
| Source | OpenRouter listing | OpenRouter listing |
Questions
Is DeepSeek V3.2 Exp better than GPT-5.2-Codex for coding?
Only GPT-5.2-Codex has a coding score, 45.4 on the Vibe Coding Index; DeepSeek V3.2 Exp has none, so the two cannot be ranked on coding. Pick DeepSeek V3.2 Exp for output tokens 34.1x cheaper ($0.41 vs $14.00 per million).
Which is cheaper, DeepSeek V3.2 Exp or GPT-5.2-Codex?
DeepSeek V3.2 Exp costs $0.27 per million input tokens and $0.41 per million output tokens; GPT-5.2-Codex costs $1.75 and $14.00. Cost per solved task is known for GPT-5.2-Codex only.
Which has the bigger context window, DeepSeek V3.2 Exp or GPT-5.2-Codex?
GPT-5.2-Codex, with 400K tokens against 164K.
How many benchmarks is the DeepSeek V3.2 Exp vs GPT-5.2-Codex comparison based on?
9 benchmarks have a result for both models as of October 22, 2026. DeepSeek V3.2 Exp's coding index rests on 1 independent benchmark and GPT-5.2-Codex's on 3. Where only one model has a result, the page says so instead of naming a winner.