DeepSeek V3.1 Terminus vs GPT-5.4 Nano
GPT-5.4 Nano leads DeepSeek V3.1 Terminus on the Artificial Analysis Intelligence Index, 20.7 to 14.8, and DeepSeek V3.1 Terminus costs 1.2x less per output token ($1.0142 vs $1.25 per million). Updated October 22, 2026; 34 benchmarks have a result for both.
DeepSeek V3.1 Terminus or GPT-5.4 Nano: which to pick
Only GPT-5.4 Nano has a coding score, 44.8 on the Vibe Coding Index; DeepSeek V3.1 Terminus has none, so the two cannot be ranked on coding.
Pick DeepSeek V3.1 Terminus for output tokens 1.2x cheaper ($1.0142 vs $1.25 per million) and work measured by LMArena Text: coding or LMArena Text: software and IT, where it leads.
- Shared results
- 34
- DeepSeek V3.1 Terminus only
- 11
- GPT-5.4 Nano only
- 51
- Like-for-like categories
- 7 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-5.4 Nano has a coding score, so no call is made.
One side not measured
Intelligence
AA Intelligence Index
GPT-5.4 Nano
GPT-5.4 Nano leads, 20.7 against 14.8.
Same board, both measured
Agentic work
AA Agentic Index
GPT-5.4 Nano
GPT-5.4 Nano leads, 16.0 against 6.8.
Same board, both measured
Reasoning
GPQA Diamond
No call
Only GPT-5.4 Nano has a GPQA Diamond result, 77.5%.
Directional only
Math
AIME
No call
Only GPT-5.4 Nano has a AIME result, 88.8%.
Directional only
Knowledge
MMLU Pro
No call
Only GPT-5.4 Nano has a MMLU Pro result, 77.2%.
Directional only
Web design
Design Arena: websites
No call
Only DeepSeek V3.1 Terminus has a Design Arena: websites result, 1187.
Directional only
Long documents
Listed context window
GPT-5.4 Nano
GPT-5.4 Nano takes 400K tokens against 164K.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
DeepSeek V3.1 Terminus
DeepSeek V3.1 Terminus $0.0008, GPT-5.4 Nano $0.0008 per turn.
List prices
Cost per solved task
One SWE-bench Verified task, solved
No call
Only GPT-5.4 Nano has the number, $0.14.
Directional only
At a glance
- Coding index
- DeepSeek V3.1 Terminus not benchmarked, GPT-5.4 Nano 44.8 from 2 benchmarks.
- Widest category
- Agentic work, 24.0 points apart on average over 2 shared benchmarks.
- Widest single gap
- τ²-Bench Telecom: DeepSeek V3.1 Terminus 37.1% against GPT-5.4 Nano 76.0%, 38.9 points apart on a 0 to 100 scale.
- Output price
- DeepSeek V3.1 Terminus $1.0142 against GPT-5.4 Nano $1.25 per million output tokens, 1.2x apart.
- Context window
- DeepSeek V3.1 Terminus takes 164K tokens, GPT-5.4 Nano 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 Terminus | Bars | GPT-5.4 Nano | Ties |
|---|---|---|---|---|
| Coding | 2 | 4 | ||
| Intelligence | 0 | 1 | ||
| Agentic work | 0 | 2 | ||
| Reasoning | 3 | 2 | ||
| Math | 0 | 1 | ||
| Knowledge | 4 | 0 | ||
| General | 12 | 3 | ||
| All shared boards | 21 | 13 |
CodingLike-for-likeSix of these boards feed the Vibe Coding Index24
| Benchmark | DeepSeek V3.1 Terminus | GPT-5.4 Nano | Leader |
|---|---|---|---|
| AA Coding Index | 43.5 | 56.1 | GPT-5.4 Nano |
| LiveCodeBench (Vals AI) | Not measured | 84.0% | Only GPT-5.4 Nano measured |
| SWE-bench Verified (Vals AI) | Not measured | 69.8% | Only GPT-5.4 Nano measured |
| Vibe Code Bench (Vals AI) | Not measured | 26.1% | Only GPT-5.4 Nano measured |
| Code migration (Vals AI) | Not measured | 14.5% | Only GPT-5.4 Nano measured |
| Terminal-Bench 2.1 (Vals AI) | Not measured | 41.6% | Only GPT-5.4 Nano measured |
| LiveCodeBench (Artificial Analysis run) | 79.8% | Not measured | Only DeepSeek V3.1 Terminus measured |
| SciCode | 38.0% | 47.2% | GPT-5.4 Nano |
| Terminal-Bench Hard (Artificial Analysis run) | 31.8% | 42.4% | GPT-5.4 Nano |
| WeirdML | Not measured | 49.2% | Only GPT-5.4 Nano measured |
| ALE-Bench | 745.2 | 1004.5 | GPT-5.4 Nano |
| LMArena Text: coding | 1425 | 1404 | DeepSeek V3.1 Terminus |
| LMArena Text: software and IT | 1426 | 1402 | DeepSeek V3.1 Terminus |
| Neither model has a result on the other 26 boards here | |||
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index01
| Benchmark | DeepSeek V3.1 Terminus | GPT-5.4 Nano | Leader |
|---|---|---|---|
| AA Intelligence Index | 14.8 | 20.7 | GPT-5.4 Nano |
| Epoch Capabilities Index | Not measured | 146.0 | Only GPT-5.4 Nano measured |
Agentic workLike-for-likePublic boards, shown as published, kept out of the index02
| Benchmark | DeepSeek V3.1 Terminus | GPT-5.4 Nano | Leader |
|---|---|---|---|
| AA Agentic Index | 6.8 | 16.0 | GPT-5.4 Nano |
| Finance Agent (Vals AI) | Not measured | 38.2% | Only GPT-5.4 Nano measured |
| Tax Agent Bench (Vals AI) | Not measured | 5.0% | Only GPT-5.4 Nano measured |
| Legal Research Bench (Vals AI) | Not measured | 6.3% | Only GPT-5.4 Nano measured |
| Excel Modeling Benchmark (Vals AI) | Not measured | 44.8% | Only GPT-5.4 Nano measured |
| τ²-Bench Telecom | 37.1% | 76.0% | GPT-5.4 Nano |
| Neither model has a result on the other 35 boards here | |||
ReasoningLike-for-likePublic boards, shown as published, kept out of the index32
| Benchmark | DeepSeek V3.1 Terminus | GPT-5.4 Nano | Leader |
|---|---|---|---|
| GPQA Diamond | Not measured | 77.5% | Only GPT-5.4 Nano measured |
| MMMU Pro (Vals AI) | Not measured | 73.6% | Only GPT-5.4 Nano measured |
| GPQA Diamond (Artificial Analysis run) | 79.2% | 81.7% | GPT-5.4 Nano |
| Humanity's Last Exam (Artificial Analysis run) | 16.4% | 28.3% | GPT-5.4 Nano |
| GPQA Diamond (Epoch AI run) | Not measured | 78.5% | Only GPT-5.4 Nano measured |
| ARC-AGI | Not measured | 51.5% | Only GPT-5.4 Nano measured |
| ARC-AGI-2 | Not measured | 5.7% | Only GPT-5.4 Nano measured |
| Chess puzzles | Not measured | 45.0% | Only GPT-5.4 Nano measured |
| Mystery game puzzles | Not measured | 9.0% | Only GPT-5.4 Nano measured |
| LMArena Text: hard prompts | 1426 | 1381 | DeepSeek V3.1 Terminus |
| LMArena Text: expert | Not measured | 1396 | Only GPT-5.4 Nano measured |
| LMArena Text: hard prompts in English | 1432 | 1387 | DeepSeek V3.1 Terminus |
| LMArena Vision: diagrams | Not measured | 1210 | Only GPT-5.4 Nano measured |
| LMArena Vision: homework | Not measured | 1225 | Only GPT-5.4 Nano measured |
| Kagi LLM Benchmark | 57.4% | 39.7% | DeepSeek V3.1 Terminus |
| Neither model has a result on the other 7 boards here | |||
MathLike-for-likePublic boards, shown as published, kept out of the index01
| Benchmark | DeepSeek V3.1 Terminus | GPT-5.4 Nano | Leader |
|---|---|---|---|
| AIME | Not measured | 88.8% | Only GPT-5.4 Nano measured |
| AA Math Index | 89.7 | Not measured | Only DeepSeek V3.1 Terminus measured |
| AIME 2025 (Artificial Analysis run) | 89.7% | Not measured | Only DeepSeek V3.1 Terminus measured |
| OTIS Mock AIME | Not measured | 87.8% | Only GPT-5.4 Nano measured |
| FrontierMath, tiers 1 to 3 | Not measured | 44.9% | Only GPT-5.4 Nano measured |
| FrontierMath, tier 4 | Not measured | 12.2% | Only GPT-5.4 Nano measured |
| ProofBench | Not measured | 5.0% | Only GPT-5.4 Nano measured |
| LMArena Text: math | 1402 | 1405 | GPT-5.4 Nano |
| LMArena Text: mathematical work | Not measured | 1409 | Only GPT-5.4 Nano measured |
| Neither model has a result on the other 4 boards here | |||
KnowledgeLike-for-likePublic boards, shown as published, kept out of the index40
| Benchmark | DeepSeek V3.1 Terminus | GPT-5.4 Nano | Leader |
|---|---|---|---|
| MMLU Pro | Not measured | 77.2% | Only GPT-5.4 Nano measured |
| LegalBench (Vals AI) | Not measured | 77.9% | Only GPT-5.4 Nano measured |
| CaseLaw (Vals AI) | Not measured | 51.9% | Only GPT-5.4 Nano measured |
| CorpFin (Vals AI) | Not measured | 61.2% | Only GPT-5.4 Nano measured |
| TaxEval (Vals AI) | Not measured | 67.4% | Only GPT-5.4 Nano measured |
| MortgageTax (Vals AI) | Not measured | 59.1% | Only GPT-5.4 Nano measured |
| MedCode (Vals AI) | Not measured | 41.0% | Only GPT-5.4 Nano measured |
| MMLU Pro (Artificial Analysis run) | 85.1% | Not measured | Only DeepSeek V3.1 Terminus measured |
| SimpleQA Verified | Not measured | 11.7% | Only GPT-5.4 Nano measured |
| LMArena Text: science | 1451 | 1374 | DeepSeek V3.1 Terminus |
| LMArena Text: medicine and healthcare | 1468 | 1369 | DeepSeek V3.1 Terminus |
| LMArena Text: legal and government | 1457 | 1368 | DeepSeek V3.1 Terminus |
| LMArena Text: business and finance | 1414 | 1366 | DeepSeek V3.1 Terminus |
| Neither model has a result on the other 5 boards here | |||
Web designDirectional onlyPublic boards, shown as published, kept out of the index7 boards, none shared
| Benchmark | DeepSeek V3.1 Terminus | GPT-5.4 Nano | Leader |
|---|---|---|---|
| Design Arena: websites | 1187 | Not measured | Only DeepSeek V3.1 Terminus measured |
| Design Arena: UI components | 1182 | Not measured | Only DeepSeek V3.1 Terminus measured |
| Design Arena: game dev | 1131 | Not measured | Only DeepSeek V3.1 Terminus measured |
| Design Arena: data viz | 1168 | Not measured | Only DeepSeek V3.1 Terminus measured |
| Design Arena: 3D | 1141 | Not measured | Only DeepSeek V3.1 Terminus measured |
| Design Arena: all code categories | 1175 | Not measured | Only DeepSeek V3.1 Terminus measured |
| Design Arena: SVG | 1065 | Not measured | Only DeepSeek V3.1 Terminus measured |
| Neither model has a result on the other 2 boards here | |||
GeneralLike-for-likePublic boards, shown as published, kept out of the index123
| Benchmark | DeepSeek V3.1 Terminus | GPT-5.4 Nano | Leader |
|---|---|---|---|
| SAGE (Vals AI) | Not measured | 38.1% | Only GPT-5.4 Nano measured |
| MedScribe (Vals AI) | Not measured | 77.1% | Only GPT-5.4 Nano measured |
| Vals Multimodal Index | Not measured | 47.6% | Only GPT-5.4 Nano measured |
| IFBench | 57.0% | 75.9% | GPT-5.4 Nano |
| Long context reasoning (AA-LCR) | 69.3% | 76.7% | GPT-5.4 Nano |
| DTBench | 81.3% | 80.3% | DeepSeek V3.1 Terminus |
| LMCA | 28.6% | 36.9% | GPT-5.4 Nano |
| LMArena Text | 1419 | 1372 | DeepSeek V3.1 Terminus |
| LMArena Text: instruction following | 1404 | 1362 | DeepSeek V3.1 Terminus |
| LMArena Text: creative writing | 1403 | 1315 | DeepSeek V3.1 Terminus |
| LMArena Vision | Not measured | 1196 | Only GPT-5.4 Nano measured |
| LMArena Text: English | 1429 | 1381 | DeepSeek V3.1 Terminus |
| LMArena Text: non-English | 1408 | 1359 | DeepSeek V3.1 Terminus |
| LMArena Text: Chinese | Not measured | 1391 | Only GPT-5.4 Nano measured |
| LMArena Text: German | Not measured | 1368 | Only GPT-5.4 Nano measured |
| LMArena Text: Japanese | Not measured | 1343 | Only GPT-5.4 Nano measured |
| LMArena Text: Korean | Not measured | 1321 | Only GPT-5.4 Nano measured |
| LMArena Text: French | Not measured | 1395 | Only GPT-5.4 Nano measured |
| LMArena Text: Spanish | Not measured | 1371 | Only GPT-5.4 Nano measured |
| LMArena Text: Russian | 1435 | 1363 | DeepSeek V3.1 Terminus |
| LMArena Text: Polish | 1427 | 1366 | DeepSeek V3.1 Terminus |
| LMArena Text: multi-turn | 1411 | 1382 | DeepSeek V3.1 Terminus |
| LMArena Text: longer queries | 1422 | 1365 | DeepSeek V3.1 Terminus |
| LMArena Text: writing and language | 1409 | 1338 | DeepSeek V3.1 Terminus |
| LMArena Text: entertainment and media | 1391 | 1325 | DeepSeek V3.1 Terminus |
| LMArena Vision: English | Not measured | 1202 | Only GPT-5.4 Nano measured |
| LMArena Vision: Chinese | Not measured | 1232 | Only GPT-5.4 Nano measured |
| LMArena Vision: reading text | Not measured | 1204 | Only GPT-5.4 Nano measured |
| LMArena Vision: humor | Not measured | 1161 | Only GPT-5.4 Nano measured |
| LMArena Vision: recognizing things | Not measured | 1141 | Only GPT-5.4 Nano measured |
| LMArena Vision: creative writing | Not measured | 1159 | Only GPT-5.4 Nano measured |
| Neither model has a result on the other 15 boards here | |||
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | DeepSeek V3.1 Terminus | GPT-5.4 Nano | 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 Terminus costs less
Repository review
50K input + 3K output tokens
GPT-5.4 Nano costs less
Agent loop
200K input + 10K output tokens
GPT-5.4 Nano 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 Terminus | OpenAI GPT-5.4 Nano |
|---|---|---|
| Maker | DeepSeek | OpenAI |
| Weights | Open weights | No public weights listed |
| Released | September 22, 2025 | March 17, 2026 |
| Context window | 164K | 400K |
| Input $/Mtok | $0.3063 | $0.20 |
| Output $/Mtok | $1.0142 | $1.25 |
| Coding index | Not benchmarked | 44.8 |
| Independent measurements behind it | 0 | 2 |
| Cost per solved task | Not computable | $0.14 |
| Source | OpenRouter listing | OpenRouter listing |
Questions
Is DeepSeek V3.1 Terminus better than GPT-5.4 Nano for coding?
Only GPT-5.4 Nano has a coding score, 44.8 on the Vibe Coding Index; DeepSeek V3.1 Terminus has none, so the two cannot be ranked on coding. Pick DeepSeek V3.1 Terminus for output tokens 1.2x cheaper ($1.0142 vs $1.25 per million) and work measured by LMArena Text: coding or LMArena Text: software and IT, where it leads.
Which is cheaper, DeepSeek V3.1 Terminus or GPT-5.4 Nano?
DeepSeek V3.1 Terminus costs $0.3063 per million input tokens and $1.0142 per million output tokens; GPT-5.4 Nano costs $0.20 and $1.25. Cost per solved task is known for GPT-5.4 Nano only.
Which has the bigger context window, DeepSeek V3.1 Terminus or GPT-5.4 Nano?
GPT-5.4 Nano, with 400K tokens against 164K.
How many benchmarks is the DeepSeek V3.1 Terminus vs GPT-5.4 Nano comparison based on?
34 benchmarks have a result for both models as of October 22, 2026. DeepSeek V3.1 Terminus's coding index rests on 0 independent benchmarks and GPT-5.4 Nano's on 2. Where only one model has a result, the page says so instead of naming a winner.