DeepSeek V4 Pro 0813 vs GPT-4o-mini (2024-07-18)
Updated October 21, 2026. 32 benchmarks have a result for both. Coding is ranked on the Vibe Coding Index; every other board is shown as published.
Decision reading
Pick DeepSeek V4 Pro 0813 for coding: it scores 54.6 on the Vibe Coding Index against 3.6 for GPT-4o-mini (2024-07-18).
Pick GPT-4o-mini (2024-07-18) when output tokens cost 3.3x less.
- Shared results
- 32
- DeepSeek V4 Pro 0813 only
- 46
- GPT-4o-mini (2024-07-18) only
- 14
- Like-for-like categories
- 5 of 9
Shared results can support a head-to-head reading. Results only one model has describe coverage, not superiority.
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, four public coding benchmarks
DeepSeek V4 Pro 0813
DeepSeek V4 Pro 0813 scores 54.6 against 3.6.
One benchmark behind each score
Intelligence
AA Intelligence Index
No call
Only DeepSeek V4 Pro 0813 has a AA Intelligence Index result, 36.0.
Directional only
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
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 128K.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
GPT-4o-mini (2024-07-18)
DeepSeek V4 Pro 0813 $0.0016, GPT-4o-mini (2024-07-18) $0.0004 per turn.
List prices
Cost per solved task
One SWE-bench Verified task, solved
No call
Only DeepSeek V4 Pro 0813 has the number, $0.10.
Directional only
At a glance
- Coding index
- DeepSeek V4 Pro 0813 54.6 from 1 benchmark, GPT-4o-mini (2024-07-18) 3.6 from 1 benchmark.
- Widest category
- Math, 53.2 points apart on average over 4 shared benchmarks.
- Widest single gap
- OTIS Mock AIME: DeepSeek V4 Pro 0813 98.6% against GPT-4o-mini (2024-07-18) 6.9%, 91.7 points apart on a 0 to 100 scale.
- Output price
- DeepSeek V4 Pro 0813 $1.98 against GPT-4o-mini (2024-07-18) $0.60 per million output tokens, 3.3x apart.
- Context window
- DeepSeek V4 Pro 0813 takes 1.05M tokens, GPT-4o-mini (2024-07-18) 128K.
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-4o-mini (2024-07-18) | Ties |
|---|---|---|---|---|
| Coding | 3 | 0 | ||
| Reasoning | 6 | 0 | ||
| Math | 4 | 0 | ||
| Knowledge | 5 | 0 | ||
| General | 14 | 0 | ||
| All shared boards | 32 | 0 |
CodingLike-for-likeFour of these boards feed the Vibe Coding Index30
| Benchmark | DeepSeek V4 Pro 0813 | GPT-4o-mini (2024-07-18) | Leader |
|---|---|---|---|
| Aider polyglot | Not measured | 3.6% | Only GPT-4o-mini (2024-07-18) measured |
| LMArena WebDev | 1582 | Not measured | Only DeepSeek V4 Pro 0813 measured |
| AA Coding Index | 68.8 | Not measured | Only DeepSeek V4 Pro 0813 measured |
| LiveCodeBench (Vals AI) | 87.5% | Not measured | Only DeepSeek V4 Pro 0813 measured |
| SWE-bench Verified (Vals AI) | 96.4% | Not measured | Only DeepSeek V4 Pro 0813 measured |
| Terminal-Bench 4 (Vals AI) | 14.1% | Not measured | Only DeepSeek V4 Pro 0813 measured |
| Vibe Code Bench (Vals AI) | 82.3% | Not measured | Only DeepSeek V4 Pro 0813 measured |
| 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% | Not measured | Only DeepSeek V4 Pro 0813 measured |
| 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 |
| FrontierCode | 28.5% | Not measured | Only DeepSeek V4 Pro 0813 measured |
| WeirdML | 66.2% | 11.8% | DeepSeek V4 Pro 0813 |
| ALE-Bench | 1403.2 | Not measured | Only DeepSeek V4 Pro 0813 measured |
| LMArena Text: coding | 1469 | 1290 | DeepSeek V4 Pro 0813 |
| LMArena Text: software and IT | 1462 | 1291 | DeepSeek V4 Pro 0813 |
| LMArena WebDev: React | 1588 | Not measured | Only DeepSeek V4 Pro 0813 measured |
| LMArena WebDev: HTML | 1566 | Not measured | Only DeepSeek V4 Pro 0813 measured |
| Neither model has a result on the other 16 boards here | |||
IntelligenceDirectional onlyPublic boards, shown as published, kept out of the index2 boards, none shared
| Benchmark | DeepSeek V4 Pro 0813 | GPT-4o-mini (2024-07-18) | Leader |
|---|---|---|---|
| AA Intelligence Index | 36.0 | Not measured | Only DeepSeek V4 Pro 0813 measured |
| Epoch Capabilities Index | 155.6 | Not measured | Only DeepSeek V4 Pro 0813 measured |
Agentic workDirectional onlyPublic boards, shown as published, kept out of the index10 boards, none shared
| Benchmark | DeepSeek V4 Pro 0813 | GPT-4o-mini (2024-07-18) | Leader |
|---|---|---|---|
| AA Agentic Index | 41.3 | Not measured | Only DeepSeek V4 Pro 0813 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 |
| APEX-Agents | 47.3% | Not measured | Only DeepSeek V4 Pro 0813 measured |
| BALROG | Not measured | 17.4% | Only GPT-4o-mini (2024-07-18) measured |
| Neither model has a result on the other 29 boards here | |||
ReasoningLike-for-likePublic boards, shown as published, kept out of the index60
| Benchmark | DeepSeek V4 Pro 0813 | GPT-4o-mini (2024-07-18) | Leader |
|---|---|---|---|
| GPQA Diamond | 92.4% | Not measured | Only DeepSeek V4 Pro 0813 measured |
| GPQA Diamond (Artificial Analysis run) | 92.8% | Not measured | Only DeepSeek V4 Pro 0813 measured |
| Humanity's Last Exam (Artificial Analysis run) | 41.0% | Not measured | Only DeepSeek V4 Pro 0813 measured |
| GPQA Diamond (Epoch AI run) | 91.7% | 37.7% | DeepSeek V4 Pro 0813 |
| 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 |
| SimpleBench | Not measured | 10.7% | Only GPT-4o-mini (2024-07-18) measured |
| Chess puzzles | 46.0% | 3.0% | DeepSeek V4 Pro 0813 |
| Mystery game puzzles | 43.0% | 12.0% | DeepSeek V4 Pro 0813 |
| VPCT | Not measured | 34.0% | Only GPT-4o-mini (2024-07-18) measured |
| LMArena Text: hard prompts | 1458 | 1267 | DeepSeek V4 Pro 0813 |
| LMArena Text: expert | 1459 | 1234 | DeepSeek V4 Pro 0813 |
| LMArena Text: hard prompts in English | 1463 | 1278 | DeepSeek V4 Pro 0813 |
| Neither model has a result on the other 8 boards here | |||
MathLike-for-likePublic boards, shown as published, kept out of the index40
| Benchmark | DeepSeek V4 Pro 0813 | GPT-4o-mini (2024-07-18) | Leader |
|---|---|---|---|
| OTIS Mock AIME | 98.6% | 6.9% | DeepSeek V4 Pro 0813 |
| FrontierMath, tiers 1 to 3 | 64.6% | 0.7% | DeepSeek V4 Pro 0813 |
| FrontierMath, tier 4 | 26.8% | Not measured | Only DeepSeek V4 Pro 0813 measured |
| ProofBench | 50.0% | Not measured | Only DeepSeek V4 Pro 0813 measured |
| MATH level 5 | Not measured | 52.6% | Only GPT-4o-mini (2024-07-18) measured |
| LMArena Text: math | 1441 | 1267 | DeepSeek V4 Pro 0813 |
| LMArena Text: mathematical work | 1443 | 1271 | DeepSeek V4 Pro 0813 |
| Neither model has a result on the other 6 boards here | |||
KnowledgeLike-for-likePublic boards, shown as published, kept out of the index50
| Benchmark | DeepSeek V4 Pro 0813 | GPT-4o-mini (2024-07-18) | 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% | 8.3% | DeepSeek V4 Pro 0813 |
| MMLU | Not measured | 76.7% | Only GPT-4o-mini (2024-07-18) measured |
| GeoBench | Not measured | 64.0% | Only GPT-4o-mini (2024-07-18) measured |
| LMArena Text: science | 1457 | 1286 | DeepSeek V4 Pro 0813 |
| LMArena Text: medicine and healthcare | 1446 | 1282 | DeepSeek V4 Pro 0813 |
| LMArena Text: legal and government | 1459 | 1305 | DeepSeek V4 Pro 0813 |
| LMArena Text: business and finance | 1442 | 1265 | DeepSeek V4 Pro 0813 |
| Neither model has a result on the other 5 boards here | |||
Web designNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | DeepSeek V4 Pro 0813 | GPT-4o-mini (2024-07-18) | Leader |
|---|---|---|---|
| Neither model has a result on 9 boards here | |||
GeneralLike-for-likePublic boards, shown as published, kept out of the index140
| Benchmark | DeepSeek V4 Pro 0813 | GPT-4o-mini (2024-07-18) | 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 |
| Long context reasoning (AA-LCR) | 80.3% | Not measured | Only DeepSeek V4 Pro 0813 measured |
| DTBench | 93.9% | Not measured | Only DeepSeek V4 Pro 0813 measured |
| LMCA | 45.5% | Not measured | Only DeepSeek V4 Pro 0813 measured |
| Creative writing (Lech Mazur) | Not measured | 67.2% | Only GPT-4o-mini (2024-07-18) measured |
| LMArena Text | 1446 | 1286 | DeepSeek V4 Pro 0813 |
| LMArena Text: instruction following | 1448 | 1258 | DeepSeek V4 Pro 0813 |
| LMArena Text: creative writing | 1439 | 1268 | DeepSeek V4 Pro 0813 |
| LMArena Vision | Not measured | 1066 | Only GPT-4o-mini (2024-07-18) measured |
| LMArena Text: English | 1457 | 1301 | DeepSeek V4 Pro 0813 |
| LMArena Text: non-English | 1431 | 1266 | DeepSeek V4 Pro 0813 |
| LMArena Text: Chinese | 1481 | 1265 | DeepSeek V4 Pro 0813 |
| LMArena Text: German | Not measured | 1272 | Only GPT-4o-mini (2024-07-18) measured |
| LMArena Text: Japanese | Not measured | 1216 | Only GPT-4o-mini (2024-07-18) measured |
| LMArena Text: Korean | 1449 | 1195 | DeepSeek V4 Pro 0813 |
| LMArena Text: French | 1446 | 1297 | DeepSeek V4 Pro 0813 |
| LMArena Text: Spanish | 1435 | 1276 | DeepSeek V4 Pro 0813 |
| LMArena Text: Russian | 1448 | 1275 | DeepSeek V4 Pro 0813 |
| LMArena Text: Polish | Not measured | 1244 | Only GPT-4o-mini (2024-07-18) measured |
| LMArena Text: multi-turn | 1454 | 1285 | DeepSeek V4 Pro 0813 |
| LMArena Text: longer queries | 1458 | 1289 | DeepSeek V4 Pro 0813 |
| LMArena Text: writing and language | 1443 | 1282 | DeepSeek V4 Pro 0813 |
| LMArena Text: entertainment and media | 1424 | 1253 | DeepSeek V4 Pro 0813 |
| LMArena Vision: English | Not measured | 1072 | Only GPT-4o-mini (2024-07-18) measured |
| LMArena Vision: Chinese | Not measured | 1033 | Only GPT-4o-mini (2024-07-18) measured |
| Neither model has a result on the other 20 boards here | |||
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | DeepSeek V4 Pro 0813 | GPT-4o-mini (2024-07-18) | 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
GPT-4o-mini (2024-07-18) costs less
Repository review
50K input + 3K output tokens
GPT-4o-mini (2024-07-18) costs less
Agent loop
200K input + 10K output tokens
GPT-4o-mini (2024-07-18) 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-4o-mini (2024-07-18) |
|---|---|---|
| Maker | DeepSeek | OpenAI |
| Weights | Open weights | No public weights listed |
| Released | August 12, 2026 | July 18, 2024 |
| Context window | 1.05M | 128K |
| Input $/Mtok | $0.66 | $0.15 |
| Output $/Mtok | $1.98 | $0.60 |
| Coding index | 54.6 | 3.6 |
| Independent measurements behind it | 1 | 1 |
| Cost per solved task | $0.10 | Not computable |
| Source | OpenRouter listing | OpenRouter listing |
Questions
Is DeepSeek V4 Pro 0813 better than GPT-4o-mini (2024-07-18) for coding?
Pick DeepSeek V4 Pro 0813 for coding: it scores 54.6 on the Vibe Coding Index against 3.6 for GPT-4o-mini (2024-07-18). Pick GPT-4o-mini (2024-07-18) when output tokens cost 3.3x less.
Which is cheaper, DeepSeek V4 Pro 0813 or GPT-4o-mini (2024-07-18)?
DeepSeek V4 Pro 0813 costs $0.66 per million input tokens and $1.98 per million output tokens; GPT-4o-mini (2024-07-18) costs $0.15 and $0.60. Cost per solved task is known for DeepSeek V4 Pro 0813 only.
Which has the bigger context window, DeepSeek V4 Pro 0813 or GPT-4o-mini (2024-07-18)?
DeepSeek V4 Pro 0813, with 1.05M tokens against 128K.
How many benchmarks is the DeepSeek V4 Pro 0813 vs GPT-4o-mini (2024-07-18) comparison based on?
32 benchmarks have a result for both models as of October 21, 2026. DeepSeek V4 Pro 0813's coding index rests on 1 independent benchmark and GPT-4o-mini (2024-07-18)'s on 1. Where only one model has a result, the page says so instead of naming a winner.