UI-TARS 7B vs Kimi K2 0905
UI-TARS 7B costs 12.5x less per output token than Kimi K2 0905 ($0.20 vs $2.50 per million). Updated October 22, 2026; 1 benchmarks have a result for both.
UI-TARS 7B or Kimi K2 0905: 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 12.5x cheaper ($0.20 vs $2.50 per million) and work measured by Kagi LLM Benchmark, where it leads.
- Shared results
- 1
- UI-TARS 7B only
- 0
- Kimi K2 0905 only
- 16
- 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
Intelligence
AA Intelligence Index
No call
Only Kimi K2 0905 has a AA Intelligence Index result, 15.3.
Directional only
Web design
Design Arena: websites
No call
Only Kimi K2 0905 has a Design Arena: websites result, 1088.
Directional only
Long documents
Listed context window
Kimi K2 0905
Kimi K2 0905 takes 262K tokens against 128K.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
UI-TARS 7B
UI-TARS 7B $0.0002, Kimi K2 0905 $0.0019 per turn.
List prices
At a glance
- Coding index
- UI-TARS 7B not benchmarked, Kimi K2 0905 not benchmarked.
- Widest category
- Reasoning, 31.5 points apart on average over 1 shared benchmark.
- Widest single gap
- Kagi LLM Benchmark: UI-TARS 7B 20.7% against Kimi K2 0905 52.2%, 31.5 points apart on a 0 to 100 scale.
- Output price
- UI-TARS 7B $0.20 against Kimi K2 0905 $2.50 per million output tokens, 12.5x apart.
- Context window
- UI-TARS 7B takes 128K tokens, Kimi K2 0905 262K.
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 | Kimi K2 0905 | Ties |
|---|---|---|---|---|
| Reasoning | 0 | 1 | ||
| All shared boards | 0 | 1 |
CodingDirectional onlySix of these boards feed the Vibe Coding Index4 boards, none shared
| Benchmark | UI-TARS 7B | Kimi K2 0905 | Leader |
|---|---|---|---|
| LiveCodeBench (Artificial Analysis run) | Not measured | 61.0% | Only Kimi K2 0905 measured |
| Terminal-Bench Hard (Artificial Analysis run) | Not measured | 23.5% | Only Kimi K2 0905 measured |
| WeirdML | Not measured | 36.7% | Only Kimi K2 0905 measured |
| ALE-Bench | Not measured | 267.1 | Only Kimi K2 0905 measured |
| Neither model has a result on the other 39 boards here | |||
IntelligenceDirectional onlyPublic boards, shown as published, kept out of the index1 board, none shared
| Benchmark | UI-TARS 7B | Kimi K2 0905 | Leader |
|---|---|---|---|
| AA Intelligence Index | Not measured | 15.3 | Only Kimi K2 0905 measured |
| Neither model has a result on the other 1 board here | |||
Agentic workDirectional onlyPublic boards, shown as published, kept out of the index1 board, none shared
| Benchmark | UI-TARS 7B | Kimi K2 0905 | Leader |
|---|---|---|---|
| τ²-Bench Telecom | Not measured | 73.4% | Only Kimi K2 0905 measured |
| Neither model has a result on the other 41 boards here | |||
ReasoningLike-for-likePublic boards, shown as published, kept out of the index01
| Benchmark | UI-TARS 7B | Kimi K2 0905 | Leader |
|---|---|---|---|
| GPQA Diamond (Artificial Analysis run) | Not measured | 76.7% | Only Kimi K2 0905 measured |
| Humanity's Last Exam (Artificial Analysis run) | Not measured | 6.4% | Only Kimi K2 0905 measured |
| Kagi LLM Benchmark | 20.7% | 52.2% | Kimi K2 0905 |
| Neither model has a result on the other 20 boards here | |||
MathDirectional onlyPublic boards, shown as published, kept out of the index2 boards, none shared
| Benchmark | UI-TARS 7B | Kimi K2 0905 | Leader |
|---|---|---|---|
| AA Math Index | Not measured | 57.3 | Only Kimi K2 0905 measured |
| AIME 2025 (Artificial Analysis run) | Not measured | 57.3% | Only Kimi K2 0905 measured |
| Neither model has a result on the other 12 boards here | |||
KnowledgeDirectional onlyPublic boards, shown as published, kept out of the index1 board, none shared
| Benchmark | UI-TARS 7B | Kimi K2 0905 | Leader |
|---|---|---|---|
| MMLU Pro (Artificial Analysis run) | Not measured | 81.9% | Only Kimi K2 0905 measured |
| Neither model has a result on the other 19 boards here | |||
Web designDirectional onlyPublic boards, shown as published, kept out of the index2 boards, none shared
| Benchmark | UI-TARS 7B | Kimi K2 0905 | Leader |
|---|---|---|---|
| Design Arena: websites | Not measured | 1088 | Only Kimi K2 0905 measured |
| Design Arena: all code categories | Not measured | 1098 | Only Kimi K2 0905 measured |
| Neither model has a result on the other 7 boards here | |||
GeneralDirectional onlyPublic boards, shown as published, kept out of the index3 boards, none shared
| Benchmark | UI-TARS 7B | Kimi K2 0905 | Leader |
|---|---|---|---|
| IFBench | Not measured | 41.7% | Only Kimi K2 0905 measured |
| Long context reasoning (AA-LCR) | Not measured | 53.0% | Only Kimi K2 0905 measured |
| Fiction.LiveBench | Not measured | 66.7% | Only Kimi K2 0905 measured |
| Neither model has a result on the other 45 boards here | |||
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | UI-TARS 7B | Kimi K2 0905 | 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 | MoonshotAI Kimi K2 0905 |
|---|---|---|
| Maker | ByteDance | Moonshot AI |
| Weights | Open weights | Open weights |
| Released | July 22, 2025 | September 4, 2025 |
| Context window | 128K | 262K |
| Input $/Mtok | $0.10 | $0.60 |
| Output $/Mtok | $0.20 | $2.50 |
| 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 Kimi K2 0905 for coding?
Neither model has a coding score yet, so the two cannot be ranked on coding. Pick UI-TARS 7B for output tokens 12.5x cheaper ($0.20 vs $2.50 per million) and work measured by Kagi LLM Benchmark, where it leads.
Which is cheaper, UI-TARS 7B or Kimi K2 0905?
UI-TARS 7B costs $0.10 per million input tokens and $0.20 per million output tokens; Kimi K2 0905 costs $0.60 and $2.50. Cost per solved task is known for neither of them.
Which has the bigger context window, UI-TARS 7B or Kimi K2 0905?
Kimi K2 0905, with 262K tokens against 128K.
How many benchmarks is the UI-TARS 7B vs Kimi K2 0905 comparison based on?
1 benchmarks have a result for both models as of October 22, 2026. UI-TARS 7B's coding index rests on 0 independent benchmarks and Kimi K2 0905's on 0. Where only one model has a result, the page says so instead of naming a winner.