DeepSeek V3.1 vs GPT-5.1-Codex
Updated October 21, 2026. 6 benchmarks have a result for both. Coding is ranked on the Vibe Coding Index; every other board is shown as published.
Decision reading
Only GPT-5.1-Codex has a coding score, 15.3 on the Vibe Coding Index; DeepSeek V3.1 has none, so the two cannot be ranked on coding.
DeepSeek V3.1 has the edge where output tokens cost 6.7x less.
- Shared results
- 6
- DeepSeek V3.1 only
- 4
- GPT-5.1-Codex only
- 20
- Like-for-like categories
- 2 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, six public coding benchmarks
No call
Only GPT-5.1-Codex has a coding score, so no call is made.
One side not measured
Intelligence
AA Intelligence Index
GPT-5.1-Codex
GPT-5.1-Codex leads, 23.7 against 13.7.
Same board, both measured
Web design
Design Arena: websites
GPT-5.1-Codex
GPT-5.1-Codex leads, 1162 against 1123.
Same board, both measured
Long documents
Listed context window
GPT-5.1-Codex
GPT-5.1-Codex takes 400K tokens against 164K.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
DeepSeek V3.1
DeepSeek V3.1 $0.0013, GPT-5.1-Codex $0.0063 per turn.
List prices
Cost per solved task
One SWE-bench Verified task, solved
No call
Only GPT-5.1-Codex has the number, $0.892.
Directional only
At a glance
- Coding index
- DeepSeek V3.1 not benchmarked, GPT-5.1-Codex 15.3 from 2 benchmarks.
- Widest category
- Web design, 10.1 points apart on average over 5 shared benchmarks.
- Widest single gap
- Design Arena: UI components: DeepSeek V3.1 1085 against GPT-5.1-Codex 1237, 19.0 points apart on a 0 to 100 scale.
- Output price
- DeepSeek V3.1 $1.50 against GPT-5.1-Codex $10.00 per million output tokens, 6.7x apart.
- Context window
- DeepSeek V3.1 takes 164K tokens, GPT-5.1-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 V3.1 | Bars | GPT-5.1-Codex | Ties |
|---|---|---|---|---|
| Intelligence | 0 | 1 | ||
| Web design | 0 | 5 | ||
| All shared boards | 0 | 6 |
CodingDirectional onlySix of these boards feed the Vibe Coding Index9 boards, none shared
| Benchmark | DeepSeek V3.1 | GPT-5.1-Codex | Leader |
|---|---|---|---|
| SWE-bench Verified | Not measured | 66.0% | Only GPT-5.1-Codex measured |
| LMArena WebDev | Not measured | 1337 | Only GPT-5.1-Codex measured |
| LiveCodeBench (Vals AI) | Not measured | 85.5% | Only GPT-5.1-Codex measured |
| Vibe Code Bench (Vals AI) | Not measured | 13.1% | Only GPT-5.1-Codex measured |
| LiveCodeBench (Artificial Analysis run) | Not measured | 84.9% | Only GPT-5.1-Codex measured |
| Terminal-Bench Hard (Artificial Analysis run) | Not measured | 34.8% | Only GPT-5.1-Codex measured |
| Terminal-Bench | Not measured | 57.8% | Only GPT-5.1-Codex measured |
| ALE-Bench | Not measured | 1244.9 | Only GPT-5.1-Codex measured |
| LMArena WebDev: HTML | Not measured | 1346 | Only GPT-5.1-Codex measured |
| Neither model has a result on the other 29 boards here | |||
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index01
| Benchmark | DeepSeek V3.1 | GPT-5.1-Codex | Leader |
|---|---|---|---|
| AA Intelligence Index | 13.7 | 23.7 | GPT-5.1-Codex |
| Neither model has a result on the other 1 board here | |||
Agentic workDirectional onlyPublic boards, shown as published, kept out of the index4 boards, none shared
| Benchmark | DeepSeek V3.1 | GPT-5.1-Codex | Leader |
|---|---|---|---|
| Design Arena: agents building web apps | Not measured | 1018 | Only GPT-5.1-Codex measured |
| Design Arena: agents building full-stack apps | Not measured | 1086 | Only GPT-5.1-Codex measured |
| Design Arena: agents building mobile apps | Not measured | 1191 | Only GPT-5.1-Codex measured |
| τ²-Bench Telecom | Not measured | 83.0% | Only GPT-5.1-Codex measured |
| Neither model has a result on the other 35 boards here | |||
ReasoningDirectional onlyPublic boards, shown as published, kept out of the index2 boards, none shared
| Benchmark | DeepSeek V3.1 | GPT-5.1-Codex | Leader |
|---|---|---|---|
| GPQA Diamond (Artificial Analysis run) | Not measured | 86.0% | Only GPT-5.1-Codex measured |
| Humanity's Last Exam (Artificial Analysis run) | Not measured | 25.7% | Only GPT-5.1-Codex measured |
| Neither model has a result on the other 19 boards here | |||
MathDirectional onlyPublic boards, shown as published, kept out of the index2 boards, none shared
| Benchmark | DeepSeek V3.1 | GPT-5.1-Codex | Leader |
|---|---|---|---|
| AA Math Index | Not measured | 95.7 | Only GPT-5.1-Codex measured |
| AIME 2025 (Artificial Analysis run) | Not measured | 95.7% | Only GPT-5.1-Codex measured |
| Neither model has a result on the other 11 boards here | |||
KnowledgeDirectional onlyPublic boards, shown as published, kept out of the index1 board, none shared
| Benchmark | DeepSeek V3.1 | GPT-5.1-Codex | Leader |
|---|---|---|---|
| MMLU Pro (Artificial Analysis run) | Not measured | 86.0% | Only GPT-5.1-Codex measured |
| Neither model has a result on the other 16 boards here | |||
Web designLike-for-likePublic boards, shown as published, kept out of the index05
| Benchmark | DeepSeek V3.1 | GPT-5.1-Codex | Leader |
|---|---|---|---|
| Design Arena: websites | 1123 | 1162 | GPT-5.1-Codex |
| Design Arena: UI components | 1085 | 1237 | GPT-5.1-Codex |
| Design Arena: game dev | 1084 | 1138 | GPT-5.1-Codex |
| Design Arena: data viz | 1095 | 1213 | GPT-5.1-Codex |
| Design Arena: 3D | 1077 | Not measured | Only DeepSeek V3.1 measured |
| Design Arena: all code categories | 1112 | 1152 | GPT-5.1-Codex |
| Design Arena: SVG | 967 | Not measured | Only DeepSeek V3.1 measured |
| Neither model has a result on the other 2 boards here | |||
GeneralDirectional onlyPublic boards, shown as published, kept out of the index4 boards, none shared
| Benchmark | DeepSeek V3.1 | GPT-5.1-Codex | Leader |
|---|---|---|---|
| IFBench | Not measured | 70.0% | Only GPT-5.1-Codex measured |
| Long context reasoning (AA-LCR) | Not measured | 69.3% | Only GPT-5.1-Codex measured |
| DTBench | 82.7% | Not measured | Only DeepSeek V3.1 measured |
| LMCA | 24.3% | Not measured | Only DeepSeek V3.1 measured |
| Neither model has a result on the other 42 boards here | |||
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | DeepSeek V3.1 | GPT-5.1-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 V3.1 costs less
Repository review
50K input + 3K output tokens
DeepSeek V3.1 costs less
Agent loop
200K input + 10K output tokens
DeepSeek V3.1 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 V3.1 | OpenAI GPT-5.1-Codex |
|---|---|---|
| Maker | DeepSeek | OpenAI |
| Weights | Open weights | No public weights listed |
| Released | August 21, 2025 | November 13, 2025 |
| Context window | 164K | 400K |
| Input $/Mtok | $0.55 | $1.25 |
| Output $/Mtok | $1.50 | $10.00 |
| Coding index | Not benchmarked | 15.3 |
| Independent measurements behind it | 0 | 2 |
| Cost per solved task | Not computable | $0.892 |
| Source | OpenRouter listing | OpenRouter listing |
Questions
Is DeepSeek V3.1 better than GPT-5.1-Codex for coding?
Only GPT-5.1-Codex has a coding score, 15.3 on the Vibe Coding Index; DeepSeek V3.1 has none, so the two cannot be ranked on coding. DeepSeek V3.1 has the edge where output tokens cost 6.7x less.
Which is cheaper, DeepSeek V3.1 or GPT-5.1-Codex?
DeepSeek V3.1 costs $0.55 per million input tokens and $1.50 per million output tokens; GPT-5.1-Codex costs $1.25 and $10.00. Cost per solved task is known for GPT-5.1-Codex only.
Which has the bigger context window, DeepSeek V3.1 or GPT-5.1-Codex?
GPT-5.1-Codex, with 400K tokens against 164K.
How many benchmarks is the DeepSeek V3.1 vs GPT-5.1-Codex comparison based on?
6 benchmarks have a result for both models as of October 21, 2026. DeepSeek V3.1's coding index rests on 0 independent benchmarks and GPT-5.1-Codex's on 2. Where only one model has a result, the page says so instead of naming a winner.