UI-TARS 7B vs DeepSeek V3
UI-TARS 7B costs 4.8x less per output token than DeepSeek V3 ($0.20 vs $0.9594 per million). Updated October 21, 2026; 1 benchmarks have a result for both.
UI-TARS 7B or DeepSeek V3: which to pick
Neither model has a coding score yet, so the two cannot be ranked on coding.
Pick UI-TARS 7B for output tokens 4.8x cheaper ($0.20 vs $0.9594 per million) and work measured by Kagi LLM Benchmark, where it leads.
- Shared results
- 1
- UI-TARS 7B only
- 0
- DeepSeek V3 only
- 13
- Like-for-like categories
- 1 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
Neither has a coding score yet.
One side not measured
Web design
Design Arena: websites
No call
Only DeepSeek V3 has a Design Arena: websites result, 1120.
Directional only
Long documents
Listed context window
DeepSeek V3
DeepSeek V3 takes 164K tokens against 128K.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
UI-TARS 7B
UI-TARS 7B $0.0002, DeepSeek V3 $0.0008 per turn.
List prices
At a glance
- Coding index
- UI-TARS 7B not benchmarked, DeepSeek V3 not benchmarked.
- Widest category
- Reasoning, 31.6 points apart on average over 1 shared benchmark.
- Widest single gap
- Kagi LLM Benchmark: UI-TARS 7B 20.7% against DeepSeek V3 52.3%, 31.6 points apart on a 0 to 100 scale.
- Output price
- UI-TARS 7B $0.20 against DeepSeek V3 $0.9594 per million output tokens, 4.8x apart.
- Context window
- UI-TARS 7B takes 128K tokens, DeepSeek V3 164K.
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 | UI-TARS 7B | Bars | DeepSeek V3 | Ties |
|---|---|---|---|---|
| Reasoning | 0 | 1 | ||
| All shared boards | 0 | 1 |
CodingDirectional onlySix of these boards feed the Vibe Coding Index2 boards, none shared
| Benchmark | UI-TARS 7B | DeepSeek V3 | Leader |
|---|---|---|---|
| Aider polyglot | Not measured | 70.2% | Only DeepSeek V3 measured |
| SWE-bench Lite | Not measured | 30.7% | Only DeepSeek V3 measured |
| Neither model has a result on the other 37 boards here | |||
IntelligenceNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | UI-TARS 7B | DeepSeek V3 | Leader |
|---|---|---|---|
| Neither model has a result on 2 boards here | |||
Agentic workNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | UI-TARS 7B | DeepSeek V3 | Leader |
|---|---|---|---|
| Neither model has a result on 41 boards here | |||
ReasoningLike-for-likePublic boards, shown as published, kept out of the index01
| Benchmark | UI-TARS 7B | DeepSeek V3 | Leader |
|---|---|---|---|
| GPQA Diamond (Epoch AI run) | Not measured | 71.2% | Only DeepSeek V3 measured |
| Kagi LLM Benchmark | 20.7% | 52.3% | DeepSeek V3 |
| Neither model has a result on the other 20 boards here | |||
MathDirectional onlyPublic boards, shown as published, kept out of the index1 board, none shared
| Benchmark | UI-TARS 7B | DeepSeek V3 | Leader |
|---|---|---|---|
| OTIS Mock AIME | Not measured | 48.9% | Only DeepSeek V3 measured |
| Neither model has a result on the other 12 boards here | |||
KnowledgeNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | UI-TARS 7B | DeepSeek V3 | Leader |
|---|---|---|---|
| Neither model has a result on 18 boards here | |||
Web designDirectional onlyPublic boards, shown as published, kept out of the index7 boards, none shared
| Benchmark | UI-TARS 7B | DeepSeek V3 | Leader |
|---|---|---|---|
| Design Arena: websites | Not measured | 1120 | Only DeepSeek V3 measured |
| Design Arena: UI components | Not measured | 1093 | Only DeepSeek V3 measured |
| Design Arena: game dev | Not measured | 1053 | Only DeepSeek V3 measured |
| Design Arena: data viz | Not measured | 1089 | Only DeepSeek V3 measured |
| Design Arena: 3D | Not measured | 1087 | Only DeepSeek V3 measured |
| Design Arena: all code categories | Not measured | 1108 | Only DeepSeek V3 measured |
| Design Arena: SVG | Not measured | 976 | Only DeepSeek V3 measured |
| Neither model has a result on the other 2 boards here | |||
GeneralDirectional onlyPublic boards, shown as published, kept out of the index2 boards, none shared
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | UI-TARS 7B | DeepSeek V3 | 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
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 V3 |
|---|---|---|
| Maker | ByteDance | DeepSeek |
| Weights | Open weights | Open weights |
| Released | July 22, 2025 | December 26, 2024 |
| Context window | 128K | 164K |
| Input $/Mtok | $0.10 | $0.2887 |
| Output $/Mtok | $0.20 | $0.9594 |
| Coding index | Not benchmarked | Not benchmarked |
| Independent measurements behind it | 0 | 0 |
| Cost per solved task | Not computable | Not computable |
| Source | OpenRouter listing | OpenRouter listing |
Questions
Is UI-TARS 7B better than DeepSeek V3 for coding?
Neither model has a coding score yet, so the two cannot be ranked on coding. Pick UI-TARS 7B for output tokens 4.8x cheaper ($0.20 vs $0.9594 per million) and work measured by Kagi LLM Benchmark, where it leads.
Which is cheaper, UI-TARS 7B or DeepSeek V3?
UI-TARS 7B costs $0.10 per million input tokens and $0.20 per million output tokens; DeepSeek V3 costs $0.2887 and $0.9594. Cost per solved task is known for neither of them.
Which has the bigger context window, UI-TARS 7B or DeepSeek V3?
DeepSeek V3, with 164K tokens against 128K.
How many benchmarks is the UI-TARS 7B vs DeepSeek V3 comparison based on?
1 benchmarks have a result for both models as of October 21, 2026. UI-TARS 7B's coding index rests on 0 independent benchmarks and DeepSeek V3's on 0. Where only one model has a result, the page says so instead of naming a winner.