UI-TARS 7B vs DeepSeek V4 Pro 0813
UI-TARS 7B costs 9.9x less per output token than DeepSeek V4 Pro 0813 ($0.20 vs $1.98 per million). Updated October 22, 2026; no benchmark has tested both models yet.
- Coding
- No data
- Overall
- No data
- Output $/M
- $0.20
- Coding
- 56.1
- Overall
- 60.8
- Output $/M
- $1.98
UI-TARS 7B or DeepSeek V4 Pro 0813: which to pick
Only DeepSeek V4 Pro 0813 has a coding score, 56.1 on the Vibe Coding Index; UI-TARS 7B has none, so the two cannot be ranked on coding.
Pick UI-TARS 7B for output tokens 9.9x cheaper ($0.20 vs $1.98 per million).
No benchmark has tested both yet. UI-TARS 7B has results on Kagi LLM Benchmark; DeepSeek V4 Pro 0813 on LMArena WebDev, AA Coding Index and LiveCodeBench (Vals AI). Compare them on price and context instead.
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 DeepSeek V4 Pro 0813 has a coding score, so no call is made.
One side not measured
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
UI-TARS 7B
UI-TARS 7B $0.0002, DeepSeek V4 Pro 0813 $0.0016 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
- UI-TARS 7B not benchmarked, DeepSeek V4 Pro 0813 56.1 from 5 benchmarks.
- Widest category
- None yet: no category has been tested on both models.
- Widest single gap
- None yet: no benchmark has tested both models.
- Output price
- UI-TARS 7B $0.20 against DeepSeek V4 Pro 0813 $1.98 per million output tokens, 9.9x apart.
- Context window
- UI-TARS 7B takes 128K tokens, DeepSeek V4 Pro 0813 1.05M.
Benchmark by benchmark
Every board either model has a result on, a category to a fold; bars are each result on a 0 to 100 scale. Only one model is measured on each, so no row names a leader.
CodingDirectional onlySix of these boards feed the Vibe Coding Index21 boards measured
| Benchmark | UI-TARS 7B | DeepSeek V4 Pro 0813 | Leader |
|---|---|---|---|
| LMArena WebDev | Not measured | 1582 | Only DeepSeek V4 Pro 0813 measured |
| AA Coding Index | Not measured | 68.8 | Only DeepSeek V4 Pro 0813 measured |
| LiveCodeBench (Vals AI) | Not measured | 87.5% | Only DeepSeek V4 Pro 0813 measured |
| SWE-bench Verified (Vals AI) | Not measured | 96.4% | Only DeepSeek V4 Pro 0813 measured |
| Terminal-Bench 4 (Vals AI) | Not measured | 14.1% | Only DeepSeek V4 Pro 0813 measured |
| Vibe Code Bench (Vals AI) | Not measured | 82.3% | Only DeepSeek V4 Pro 0813 measured |
| Code migration (Vals AI) | Not measured | 41.5% | Only DeepSeek V4 Pro 0813 measured |
| ProgramBench (Vals AI) | Not measured | 0.0% | Only DeepSeek V4 Pro 0813 measured |
| IOI (Vals AI) | Not measured | 51.6% | Only DeepSeek V4 Pro 0813 measured |
| Terminal-Bench 2.1 (Vals AI) | Not measured | 54.7% | Only DeepSeek V4 Pro 0813 measured |
| Vibe Code Bench 1-100 (Vals AI) | Not measured | 17.5% | Only DeepSeek V4 Pro 0813 measured |
| SkillsBench (Vals AI) | Not measured | 53.8% | Only DeepSeek V4 Pro 0813 measured |
| CyberBench (Vals AI) | Not measured | 72.9% | Only DeepSeek V4 Pro 0813 measured |
| SciCode | Not measured | 51.0% | Only DeepSeek V4 Pro 0813 measured |
| FrontierCode | Not measured | 28.5% | Only DeepSeek V4 Pro 0813 measured |
| WeirdML | Not measured | 66.2% | Only DeepSeek V4 Pro 0813 measured |
| ALE-Bench | Not measured | 1403.2 | Only DeepSeek V4 Pro 0813 measured |
| LMArena Text: coding | Not measured | 1469 | Only DeepSeek V4 Pro 0813 measured |
| LMArena Text: software and IT | Not measured | 1462 | Only DeepSeek V4 Pro 0813 measured |
| LMArena WebDev: React | Not measured | 1588 | Only DeepSeek V4 Pro 0813 measured |
| LMArena WebDev: HTML | Not measured | 1566 | Only DeepSeek V4 Pro 0813 measured |
| Neither model has a result on the other 22 boards here | |||
IntelligenceDirectional onlyPublic boards, shown as published, kept out of the index2 boards measured
| Benchmark | UI-TARS 7B | DeepSeek V4 Pro 0813 | Leader |
|---|---|---|---|
| AA Intelligence Index | Not measured | 36.0 | Only DeepSeek V4 Pro 0813 measured |
| Epoch Capabilities Index | Not measured | 155.6 | Only DeepSeek V4 Pro 0813 measured |
Agentic workDirectional onlyPublic boards, shown as published, kept out of the index9 boards measured
| Benchmark | UI-TARS 7B | DeepSeek V4 Pro 0813 | Leader |
|---|---|---|---|
| AA Agentic Index | Not measured | 41.3 | Only DeepSeek V4 Pro 0813 measured |
| SREBench (Vals AI) | Not measured | 1.9% | Only DeepSeek V4 Pro 0813 measured |
| Finance Agent (Vals AI) | Not measured | 50.4% | Only DeepSeek V4 Pro 0813 measured |
| Tax Agent Bench (Vals AI) | Not measured | 26.8% | Only DeepSeek V4 Pro 0813 measured |
| Legal Research Bench (Vals AI) | Not measured | 40.9% | Only DeepSeek V4 Pro 0813 measured |
| Harvey's Legal Agent Benchmark (Vals AI) | Not measured | 10.8% | Only DeepSeek V4 Pro 0813 measured |
| Excel Modeling Benchmark (Vals AI) | Not measured | 52.8% | Only DeepSeek V4 Pro 0813 measured |
| Terminal-Bench-Science (Vals AI) | Not measured | 4.3% | Only DeepSeek V4 Pro 0813 measured |
| APEX-Agents | Not measured | 47.3% | Only DeepSeek V4 Pro 0813 measured |
| Neither model has a result on the other 33 boards here | |||
ReasoningDirectional onlyPublic boards, shown as published, kept out of the index12 boards measured
| Benchmark | UI-TARS 7B | DeepSeek V4 Pro 0813 | Leader |
|---|---|---|---|
| GPQA Diamond | Not measured | 92.4% | Only DeepSeek V4 Pro 0813 measured |
| GPQA Diamond (Artificial Analysis run) | Not measured | 92.8% | Only DeepSeek V4 Pro 0813 measured |
| Humanity's Last Exam (Artificial Analysis run) | Not measured | 41.0% | Only DeepSeek V4 Pro 0813 measured |
| GPQA Diamond (Epoch AI run) | Not measured | 91.7% | Only DeepSeek V4 Pro 0813 measured |
| ARC-AGI | Not measured | 90.5% | Only DeepSeek V4 Pro 0813 measured |
| ARC-AGI-2 | Not measured | 61.3% | Only DeepSeek V4 Pro 0813 measured |
| Chess puzzles | Not measured | 46.0% | Only DeepSeek V4 Pro 0813 measured |
| Mystery game puzzles | Not measured | 43.0% | Only DeepSeek V4 Pro 0813 measured |
| LMArena Text: hard prompts | Not measured | 1458 | Only DeepSeek V4 Pro 0813 measured |
| LMArena Text: expert | Not measured | 1459 | Only DeepSeek V4 Pro 0813 measured |
| LMArena Text: hard prompts in English | Not measured | 1463 | Only DeepSeek V4 Pro 0813 measured |
| Kagi LLM Benchmark | 20.7% | Not measured | Only UI-TARS 7B measured |
| Neither model has a result on the other 11 boards here | |||
MathDirectional onlyPublic boards, shown as published, kept out of the index6 boards measured
| Benchmark | UI-TARS 7B | DeepSeek V4 Pro 0813 | Leader |
|---|---|---|---|
| OTIS Mock AIME | Not measured | 98.6% | Only DeepSeek V4 Pro 0813 measured |
| FrontierMath, tiers 1 to 3 | Not measured | 64.6% | Only DeepSeek V4 Pro 0813 measured |
| FrontierMath, tier 4 | Not measured | 26.8% | Only DeepSeek V4 Pro 0813 measured |
| ProofBench | Not measured | 50.0% | Only DeepSeek V4 Pro 0813 measured |
| LMArena Text: math | Not measured | 1441 | Only DeepSeek V4 Pro 0813 measured |
| LMArena Text: mathematical work | Not measured | 1443 | Only DeepSeek V4 Pro 0813 measured |
| Neither model has a result on the other 8 boards here | |||
KnowledgeDirectional onlyPublic boards, shown as published, kept out of the index10 boards measured
| Benchmark | UI-TARS 7B | DeepSeek V4 Pro 0813 | Leader |
|---|---|---|---|
| MMLU Pro | Not measured | 87.0% | Only DeepSeek V4 Pro 0813 measured |
| LegalBench (Vals AI) | Not measured | 82.4% | Only DeepSeek V4 Pro 0813 measured |
| CorpFin (Vals AI) | Not measured | 65.4% | Only DeepSeek V4 Pro 0813 measured |
| TaxEval (Vals AI) | Not measured | 73.1% | Only DeepSeek V4 Pro 0813 measured |
| MedCode (Vals AI) | Not measured | 42.5% | Only DeepSeek V4 Pro 0813 measured |
| SimpleQA Verified | Not measured | 52.9% | Only DeepSeek V4 Pro 0813 measured |
| LMArena Text: science | Not measured | 1457 | Only DeepSeek V4 Pro 0813 measured |
| LMArena Text: medicine and healthcare | Not measured | 1446 | Only DeepSeek V4 Pro 0813 measured |
| LMArena Text: legal and government | Not measured | 1459 | Only DeepSeek V4 Pro 0813 measured |
| LMArena Text: business and finance | Not measured | 1442 | Only DeepSeek V4 Pro 0813 measured |
| Neither model has a result on the other 10 boards here | |||
GeneralDirectional onlyPublic boards, shown as published, kept out of the index19 boards measured
| Benchmark | UI-TARS 7B | DeepSeek V4 Pro 0813 | Leader |
|---|---|---|---|
| Vals Index | Not measured | 53.0% | Only DeepSeek V4 Pro 0813 measured |
| MedScribe (Vals AI) | Not measured | 80.2% | Only DeepSeek V4 Pro 0813 measured |
| Long context reasoning (AA-LCR) | Not measured | 80.3% | Only DeepSeek V4 Pro 0813 measured |
| DTBench | Not measured | 93.9% | Only DeepSeek V4 Pro 0813 measured |
| LMCA | Not measured | 45.5% | Only DeepSeek V4 Pro 0813 measured |
| LMArena Text | Not measured | 1446 | Only DeepSeek V4 Pro 0813 measured |
| LMArena Text: instruction following | Not measured | 1448 | Only DeepSeek V4 Pro 0813 measured |
| LMArena Text: creative writing | Not measured | 1439 | Only DeepSeek V4 Pro 0813 measured |
| LMArena Text: English | Not measured | 1457 | Only DeepSeek V4 Pro 0813 measured |
| LMArena Text: non-English | Not measured | 1431 | Only DeepSeek V4 Pro 0813 measured |
| LMArena Text: Chinese | Not measured | 1481 | Only DeepSeek V4 Pro 0813 measured |
| LMArena Text: Korean | Not measured | 1449 | Only DeepSeek V4 Pro 0813 measured |
| LMArena Text: French | Not measured | 1446 | Only DeepSeek V4 Pro 0813 measured |
| LMArena Text: Spanish | Not measured | 1435 | Only DeepSeek V4 Pro 0813 measured |
| LMArena Text: Russian | Not measured | 1448 | Only DeepSeek V4 Pro 0813 measured |
| LMArena Text: multi-turn | Not measured | 1454 | Only DeepSeek V4 Pro 0813 measured |
| LMArena Text: longer queries | Not measured | 1458 | Only DeepSeek V4 Pro 0813 measured |
| LMArena Text: writing and language | Not measured | 1443 | Only DeepSeek V4 Pro 0813 measured |
| LMArena Text: entertainment and media | Not measured | 1424 | Only DeepSeek V4 Pro 0813 measured |
| Neither model has a result on the other 29 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
UI-TARS 7B costs less
Repository review
50K input + 3K output tokens
UI-TARS 7B costs less
Agent loop
200K input + 10K output tokens
UI-TARS 7B 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 | ByteDance UI-TARS 7B | DeepSeek V4 Pro 0813 |
|---|---|---|
| Maker | ByteDance | DeepSeek |
| Weights | Open weights | Open weights |
| Released | July 22, 2025 | August 12, 2026 |
| Context window | 128K | 1.05M |
| Input $/Mtok | $0.10 | $0.66 |
| Output $/Mtok | $0.20 | $1.98 |
| Coding index | Not benchmarked | 56.1 |
| Independent measurements behind it | 0 | 5 |
| Cost per solved task | Not computable | $0.10 |
| Source | OpenRouter listing | OpenRouter listing |
Questions
Is UI-TARS 7B better than DeepSeek V4 Pro 0813 for coding?
Only DeepSeek V4 Pro 0813 has a coding score, 56.1 on the Vibe Coding Index; UI-TARS 7B has none, so the two cannot be ranked on coding. Pick UI-TARS 7B for output tokens 9.9x cheaper ($0.20 vs $1.98 per million).
Which is cheaper, UI-TARS 7B or DeepSeek V4 Pro 0813?
UI-TARS 7B costs $0.10 per million input tokens and $0.20 per million output tokens; DeepSeek V4 Pro 0813 costs $0.66 and $1.98. Cost per solved task is known for DeepSeek V4 Pro 0813 only.
Which has the bigger context window, UI-TARS 7B or DeepSeek V4 Pro 0813?
DeepSeek V4 Pro 0813, with 1.05M tokens against 128K.
Has any benchmark tested both UI-TARS 7B and DeepSeek V4 Pro 0813?
No benchmark has tested both yet. UI-TARS 7B has results on Kagi LLM Benchmark; DeepSeek V4 Pro 0813 on LMArena WebDev, AA Coding Index and LiveCodeBench (Vals AI). Compare them on price and context instead. Data as of October 22, 2026.