DeepSeek V4 Pro 0813 vs GPT-5.3-Codex
GPT-5.3-Codex leads DeepSeek V4 Pro 0813 on coding, 60.0 to 56.1 on the Vibe Coding Index, and DeepSeek V4 Pro 0813 costs 7.1x less per output token ($1.98 vs $14.00 per million). Updated October 22, 2026; 14 benchmarks have a result for both.
DeepSeek V4 Pro 0813 or GPT-5.3-Codex: which to pick
Pick GPT-5.3-Codex for coding: it scores 60.0 on the Vibe Coding Index against 56.1 for DeepSeek V4 Pro 0813, though on 3 benchmarks against 5.
Pick DeepSeek V4 Pro 0813 for output tokens 7.1x cheaper ($1.98 vs $14.00 per million) and a lower cost per solved task ($0.10 against $0.59).
- Shared results
- 14
- DeepSeek V4 Pro 0813 only
- 64
- GPT-5.3-Codex only
- 20
- Like-for-like categories
- 4 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
GPT-5.3-Codex
GPT-5.3-Codex scores 60.0 against 56.1.
5 and 3 benchmarks behind the scores
Intelligence
AA Intelligence Index
DeepSeek V4 Pro 0813
DeepSeek V4 Pro 0813 leads, 36.0 against 32.5.
Same board, both measured
Agentic work
AA Agentic Index
No call
Only DeepSeek V4 Pro 0813 has a AA Agentic Index result, 41.3.
Directional only
Reasoning
GPQA Diamond
No call
Only DeepSeek V4 Pro 0813 has a GPQA Diamond result, 92.4%.
Directional only
Knowledge
MMLU Pro
No call
Only DeepSeek V4 Pro 0813 has a MMLU Pro result, 87.0%.
Directional only
Web design
Design Arena: websites
No call
Only GPT-5.3-Codex has a Design Arena: websites result, 1162.
Directional only
General
Vals Index
No call
Only DeepSeek V4 Pro 0813 has a Vals Index result, 53.0%.
Directional only
Long documents
Listed context window
DeepSeek V4 Pro 0813
DeepSeek V4 Pro 0813 takes 1.05M tokens against 400K.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
DeepSeek V4 Pro 0813
DeepSeek V4 Pro 0813 $0.0016, GPT-5.3-Codex $0.0088 per turn.
List prices
Cost per solved task
One SWE-bench Verified task, solved
DeepSeek V4 Pro 0813
DeepSeek V4 Pro 0813 $0.10, GPT-5.3-Codex $0.59.
Published attempt cost and pass rate
At a glance
- Coding index
- DeepSeek V4 Pro 0813 56.1 from 5 benchmarks, GPT-5.3-Codex 60.0 from 3 benchmarks.
- Widest category
- Coding, 16.0 points apart on average over 9 shared benchmarks.
- Widest single gap
- LMArena WebDev: React: DeepSeek V4 Pro 0813 1588 against GPT-5.3-Codex 1395, 32.2 points apart on a 0 to 100 scale.
- Output price
- DeepSeek V4 Pro 0813 $1.98 against GPT-5.3-Codex $14.00 per million output tokens, 7.1x apart.
- Context window
- DeepSeek V4 Pro 0813 takes 1.05M tokens, GPT-5.3-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 V4 Pro 0813 | Bars | GPT-5.3-Codex | Ties |
|---|---|---|---|---|
| Coding | 6 | 3 | ||
| Intelligence | 1 | 1 | ||
| Reasoning | 1 | 1 | ||
| General | 0 | 1 | ||
| All shared boards | 8 | 6 |
CodingLike-for-likeSix of these boards feed the Vibe Coding Index63
| Benchmark | DeepSeek V4 Pro 0813 | GPT-5.3-Codex | Leader |
|---|---|---|---|
| LMArena WebDev | 1582 | 1407 | DeepSeek V4 Pro 0813 |
| AA Coding Index | 68.8 | Not measured | Only DeepSeek V4 Pro 0813 measured |
| LiveCodeBench (Vals AI) | 87.5% | 87.3% | DeepSeek V4 Pro 0813 |
| SWE-bench Verified (Vals AI) | 96.4% | 78.0% | DeepSeek V4 Pro 0813 |
| Terminal-Bench 4 (Vals AI) | 14.1% | Not measured | Only DeepSeek V4 Pro 0813 measured |
| Vibe Code Bench (Vals AI) | 82.3% | 61.8% | DeepSeek V4 Pro 0813 |
| Code migration (Vals AI) | 41.5% | Not measured | Only DeepSeek V4 Pro 0813 measured |
| ProgramBench (Vals AI) | 0.0% | Not measured | Only DeepSeek V4 Pro 0813 measured |
| IOI (Vals AI) | 51.6% | 53.8% | GPT-5.3-Codex |
| Terminal-Bench 2.1 (Vals AI) | 54.7% | Not measured | Only DeepSeek V4 Pro 0813 measured |
| Vibe Code Bench 1-100 (Vals AI) | 17.5% | Not measured | Only DeepSeek V4 Pro 0813 measured |
| SkillsBench (Vals AI) | 53.8% | Not measured | Only DeepSeek V4 Pro 0813 measured |
| CyberBench (Vals AI) | 72.9% | Not measured | Only DeepSeek V4 Pro 0813 measured |
| SciCode | 51.0% | Not measured | Only DeepSeek V4 Pro 0813 measured |
| Terminal-Bench Hard (Artificial Analysis run) | Not measured | 53.0% | Only GPT-5.3-Codex measured |
| Terminal-Bench | Not measured | 78.4% | Only GPT-5.3-Codex measured |
| FrontierCode | 28.5% | Not measured | Only DeepSeek V4 Pro 0813 measured |
| WeirdML | 66.2% | 79.3% | GPT-5.3-Codex |
| ALE-Bench | 1403.2 | 1655.2 | GPT-5.3-Codex |
| LMArena Text: coding | 1469 | Not measured | Only DeepSeek V4 Pro 0813 measured |
| LMArena Text: software and IT | 1462 | Not measured | Only DeepSeek V4 Pro 0813 measured |
| LMArena WebDev: React | 1588 | 1395 | DeepSeek V4 Pro 0813 |
| LMArena WebDev: HTML | 1566 | 1422 | DeepSeek V4 Pro 0813 |
| LMArena WebDev: from an image | Not measured | 1443 | Only GPT-5.3-Codex measured |
| Neither model has a result on the other 15 boards here | |||
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index11
| Benchmark | DeepSeek V4 Pro 0813 | GPT-5.3-Codex | Leader |
|---|---|---|---|
| AA Intelligence Index | 36.0 | 32.5 | DeepSeek V4 Pro 0813 |
| Epoch Capabilities Index | 155.6 | 157.1 | GPT-5.3-Codex |
Agentic workDirectional onlyPublic boards, shown as published, kept out of the index17 boards, none shared
| Benchmark | DeepSeek V4 Pro 0813 | GPT-5.3-Codex | Leader |
|---|---|---|---|
| AA Agentic Index | 41.3 | Not measured | Only DeepSeek V4 Pro 0813 measured |
| Design Arena: agents building web apps | Not measured | 1032 | Only GPT-5.3-Codex measured |
| Design Arena: agents building full-stack apps | Not measured | 983 | Only GPT-5.3-Codex measured |
| Design Arena: agents building mobile apps | Not measured | 1053 | Only GPT-5.3-Codex measured |
| Design Arena: agents building Android apps | Not measured | 1084 | Only GPT-5.3-Codex measured |
| Design Arena: agents building Godot games | Not measured | 1124 | Only GPT-5.3-Codex measured |
| SREBench (Vals AI) | 1.9% | Not measured | Only DeepSeek V4 Pro 0813 measured |
| Finance Agent (Vals AI) | 50.4% | Not measured | Only DeepSeek V4 Pro 0813 measured |
| Tax Agent Bench (Vals AI) | 26.8% | Not measured | Only DeepSeek V4 Pro 0813 measured |
| Legal Research Bench (Vals AI) | 40.9% | Not measured | Only DeepSeek V4 Pro 0813 measured |
| Harvey's Legal Agent Benchmark (Vals AI) | 10.8% | Not measured | Only DeepSeek V4 Pro 0813 measured |
| Excel Modeling Benchmark (Vals AI) | 52.8% | Not measured | Only DeepSeek V4 Pro 0813 measured |
| Terminal-Bench-Science (Vals AI) | 4.3% | Not measured | Only DeepSeek V4 Pro 0813 measured |
| τ²-Bench Telecom | Not measured | 86.0% | Only GPT-5.3-Codex measured |
| APEX-Agents | 47.3% | Not measured | Only DeepSeek V4 Pro 0813 measured |
| METR time horizons | Not measured | 74.5% | Only GPT-5.3-Codex measured |
| Vending-Bench 2 | Not measured | $5,940 | Only GPT-5.3-Codex measured |
| Neither model has a result on the other 24 boards here | |||
ReasoningLike-for-likePublic boards, shown as published, kept out of the index11
| Benchmark | DeepSeek V4 Pro 0813 | GPT-5.3-Codex | Leader |
|---|---|---|---|
| GPQA Diamond | 92.4% | Not measured | Only DeepSeek V4 Pro 0813 measured |
| GPQA Diamond (Artificial Analysis run) | 92.8% | 91.5% | DeepSeek V4 Pro 0813 |
| Humanity's Last Exam (Artificial Analysis run) | 41.0% | 42.5% | GPT-5.3-Codex |
| GPQA Diamond (Epoch AI run) | 91.7% | Not measured | Only DeepSeek V4 Pro 0813 measured |
| ARC-AGI | 90.5% | Not measured | Only DeepSeek V4 Pro 0813 measured |
| ARC-AGI-2 | 61.3% | Not measured | Only DeepSeek V4 Pro 0813 measured |
| Chess puzzles | 46.0% | Not measured | Only DeepSeek V4 Pro 0813 measured |
| Mystery game puzzles | 43.0% | Not measured | Only DeepSeek V4 Pro 0813 measured |
| LMArena Text: hard prompts | 1458 | Not measured | Only DeepSeek V4 Pro 0813 measured |
| LMArena Text: expert | 1459 | Not measured | Only DeepSeek V4 Pro 0813 measured |
| LMArena Text: hard prompts in English | 1463 | Not measured | Only DeepSeek V4 Pro 0813 measured |
| Neither model has a result on the other 11 boards here | |||
MathDirectional onlyPublic boards, shown as published, kept out of the index6 boards, none shared
| Benchmark | DeepSeek V4 Pro 0813 | GPT-5.3-Codex | Leader |
|---|---|---|---|
| OTIS Mock AIME | 98.6% | Not measured | Only DeepSeek V4 Pro 0813 measured |
| FrontierMath, tiers 1 to 3 | 64.6% | Not measured | Only DeepSeek V4 Pro 0813 measured |
| FrontierMath, tier 4 | 26.8% | Not measured | Only DeepSeek V4 Pro 0813 measured |
| ProofBench | 50.0% | Not measured | Only DeepSeek V4 Pro 0813 measured |
| LMArena Text: math | 1441 | Not measured | Only DeepSeek V4 Pro 0813 measured |
| LMArena Text: mathematical work | 1443 | Not measured | Only DeepSeek V4 Pro 0813 measured |
| Neither model has a result on the other 7 boards here | |||
KnowledgeDirectional onlyPublic boards, shown as published, kept out of the index10 boards, none shared
| Benchmark | DeepSeek V4 Pro 0813 | GPT-5.3-Codex | Leader |
|---|---|---|---|
| MMLU Pro | 87.0% | Not measured | Only DeepSeek V4 Pro 0813 measured |
| LegalBench (Vals AI) | 82.4% | Not measured | Only DeepSeek V4 Pro 0813 measured |
| CorpFin (Vals AI) | 65.4% | Not measured | Only DeepSeek V4 Pro 0813 measured |
| TaxEval (Vals AI) | 73.1% | Not measured | Only DeepSeek V4 Pro 0813 measured |
| MedCode (Vals AI) | 42.5% | Not measured | Only DeepSeek V4 Pro 0813 measured |
| SimpleQA Verified | 52.9% | Not measured | Only DeepSeek V4 Pro 0813 measured |
| LMArena Text: science | 1457 | Not measured | Only DeepSeek V4 Pro 0813 measured |
| LMArena Text: medicine and healthcare | 1446 | Not measured | Only DeepSeek V4 Pro 0813 measured |
| LMArena Text: legal and government | 1459 | Not measured | Only DeepSeek V4 Pro 0813 measured |
| LMArena Text: business and finance | 1442 | Not measured | Only DeepSeek V4 Pro 0813 measured |
| Neither model has a result on the other 8 boards here | |||
Web designDirectional onlyPublic boards, shown as published, kept out of the index8 boards, none shared
| Benchmark | DeepSeek V4 Pro 0813 | GPT-5.3-Codex | Leader |
|---|---|---|---|
| Design Arena: websites | Not measured | 1162 | Only GPT-5.3-Codex measured |
| Design Arena: UI components | Not measured | 1134 | Only GPT-5.3-Codex measured |
| Design Arena: game dev | Not measured | 1161 | Only GPT-5.3-Codex measured |
| Design Arena: data viz | Not measured | 1164 | Only GPT-5.3-Codex measured |
| Design Arena: 3D | Not measured | 1007 | Only GPT-5.3-Codex measured |
| Design Arena: all code categories | Not measured | 1144 | Only GPT-5.3-Codex measured |
| Design Arena: SVG | Not measured | 1129 | Only GPT-5.3-Codex measured |
| Design Arena: ASCII art | Not measured | 1152 | Only GPT-5.3-Codex measured |
| Neither model has a result on the other 1 board here | |||
GeneralLike-for-likePublic boards, shown as published, kept out of the index01
| Benchmark | DeepSeek V4 Pro 0813 | GPT-5.3-Codex | Leader |
|---|---|---|---|
| Vals Index | 53.0% | Not measured | Only DeepSeek V4 Pro 0813 measured |
| MedScribe (Vals AI) | 80.2% | Not measured | Only DeepSeek V4 Pro 0813 measured |
| IFBench | Not measured | 75.4% | Only GPT-5.3-Codex measured |
| Long context reasoning (AA-LCR) | 80.3% | 83.3% | GPT-5.3-Codex |
| DTBench | 93.9% | Not measured | Only DeepSeek V4 Pro 0813 measured |
| LMCA | 45.5% | Not measured | Only DeepSeek V4 Pro 0813 measured |
| LMArena Text | 1446 | Not measured | Only DeepSeek V4 Pro 0813 measured |
| LMArena Text: instruction following | 1448 | Not measured | Only DeepSeek V4 Pro 0813 measured |
| LMArena Text: creative writing | 1439 | Not measured | Only DeepSeek V4 Pro 0813 measured |
| LMArena Text: English | 1457 | Not measured | Only DeepSeek V4 Pro 0813 measured |
| LMArena Text: non-English | 1431 | Not measured | Only DeepSeek V4 Pro 0813 measured |
| LMArena Text: Chinese | 1481 | Not measured | Only DeepSeek V4 Pro 0813 measured |
| LMArena Text: Korean | 1449 | Not measured | Only DeepSeek V4 Pro 0813 measured |
| LMArena Text: French | 1446 | Not measured | Only DeepSeek V4 Pro 0813 measured |
| LMArena Text: Spanish | 1435 | Not measured | Only DeepSeek V4 Pro 0813 measured |
| LMArena Text: Russian | 1448 | Not measured | Only DeepSeek V4 Pro 0813 measured |
| LMArena Text: multi-turn | 1454 | Not measured | Only DeepSeek V4 Pro 0813 measured |
| LMArena Text: longer queries | 1458 | Not measured | Only DeepSeek V4 Pro 0813 measured |
| LMArena Text: writing and language | 1443 | Not measured | Only DeepSeek V4 Pro 0813 measured |
| LMArena Text: entertainment and media | 1424 | Not measured | Only DeepSeek V4 Pro 0813 measured |
| Neither model has a result on the other 26 boards here | |||
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | DeepSeek V4 Pro 0813 | GPT-5.3-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 V4 Pro 0813 costs less
Repository review
50K input + 3K output tokens
DeepSeek V4 Pro 0813 costs less
Agent loop
200K input + 10K output tokens
DeepSeek V4 Pro 0813 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 V4 Pro 0813 | OpenAI GPT-5.3-Codex |
|---|---|---|
| Maker | DeepSeek | OpenAI |
| Weights | Open weights | No public weights listed |
| Released | August 12, 2026 | February 24, 2026 |
| Context window | 1.05M | 400K |
| Input $/Mtok | $0.66 | $1.75 |
| Output $/Mtok | $1.98 | $14.00 |
| Coding index | 56.1 | 60.0 |
| Independent measurements behind it | 5 | 3 |
| Cost per solved task | $0.10 | $0.59 |
| Source | OpenRouter listing | OpenRouter listing |
Questions
Is DeepSeek V4 Pro 0813 better than GPT-5.3-Codex for coding?
Pick GPT-5.3-Codex for coding: it scores 60.0 on the Vibe Coding Index against 56.1 for DeepSeek V4 Pro 0813, though on 3 benchmarks against 5. Pick DeepSeek V4 Pro 0813 for output tokens 7.1x cheaper ($1.98 vs $14.00 per million) and a lower cost per solved task ($0.10 against $0.59).
Which is cheaper, DeepSeek V4 Pro 0813 or GPT-5.3-Codex?
DeepSeek V4 Pro 0813 costs $0.66 per million input tokens and $1.98 per million output tokens; GPT-5.3-Codex costs $1.75 and $14.00. Per solved SWE-bench Verified task, DeepSeek V4 Pro 0813 costs $0.10 and GPT-5.3-Codex $0.59.
Which has the bigger context window, DeepSeek V4 Pro 0813 or GPT-5.3-Codex?
DeepSeek V4 Pro 0813, with 1.05M tokens against 400K.
How many benchmarks is the DeepSeek V4 Pro 0813 vs GPT-5.3-Codex comparison based on?
14 benchmarks have a result for both models as of October 22, 2026. DeepSeek V4 Pro 0813's coding index rests on 5 independent benchmarks and GPT-5.3-Codex's on 3. Where only one model has a result, the page says so instead of naming a winner.