UI-TARS 7B vs GLM 4.5V
UI-TARS 7B costs 9.0x less per output token than GLM 4.5V ($0.20 vs $1.80 per million). Updated October 22, 2026; 1 benchmarks have a result for both.
UI-TARS 7B or GLM 4.5V: 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 9.0x cheaper ($0.20 vs $1.80 per million) and a larger context window (128K against 66K tokens).
- Shared results
- 1
- UI-TARS 7B only
- 0
- GLM 4.5V only
- 41
- 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 GLM 4.5V has a AA Intelligence Index result, 7.6.
Directional only
Long documents
Listed context window
UI-TARS 7B
UI-TARS 7B takes 128K tokens against 66K.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
UI-TARS 7B
UI-TARS 7B $0.0002, GLM 4.5V $0.0015 per turn.
List prices
At a glance
- Coding index
- UI-TARS 7B not benchmarked, GLM 4.5V not benchmarked.
- Widest category
- Reasoning, 39.1 points apart on average over 1 shared benchmark.
- Widest single gap
- Kagi LLM Benchmark: UI-TARS 7B 20.7% against GLM 4.5V 59.8%, 39.1 points apart on a 0 to 100 scale.
- Output price
- UI-TARS 7B $0.20 against GLM 4.5V $1.80 per million output tokens, 9.0x apart.
- Context window
- UI-TARS 7B takes 128K tokens, GLM 4.5V 66K.
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 | GLM 4.5V | 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 | GLM 4.5V | Leader |
|---|---|---|---|
| LiveCodeBench (Artificial Analysis run) | Not measured | 60.4% | Only GLM 4.5V measured |
| Terminal-Bench Hard (Artificial Analysis run) | Not measured | 6.8% | Only GLM 4.5V measured |
| LMArena Text: coding | Not measured | 1347 | Only GLM 4.5V measured |
| LMArena Text: software and IT | Not measured | 1353 | Only GLM 4.5V 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 | GLM 4.5V | Leader |
|---|---|---|---|
| AA Intelligence Index | Not measured | 7.6 | Only GLM 4.5V 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 | GLM 4.5V | Leader |
|---|---|---|---|
| τ²-Bench Telecom | Not measured | 22.5% | Only GLM 4.5V 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 | GLM 4.5V | Leader |
|---|---|---|---|
| GPQA Diamond (Artificial Analysis run) | Not measured | 68.4% | Only GLM 4.5V measured |
| Humanity's Last Exam (Artificial Analysis run) | Not measured | 6.3% | Only GLM 4.5V measured |
| LMArena Text: hard prompts | Not measured | 1335 | Only GLM 4.5V measured |
| LMArena Text: expert | Not measured | 1353 | Only GLM 4.5V measured |
| LMArena Text: hard prompts in English | Not measured | 1357 | Only GLM 4.5V measured |
| LMArena Vision: diagrams | Not measured | 1151 | Only GLM 4.5V measured |
| LMArena Vision: homework | Not measured | 1163 | Only GLM 4.5V measured |
| Kagi LLM Benchmark | 20.7% | 59.8% | GLM 4.5V |
| Neither model has a result on the other 15 boards here | |||
MathDirectional onlyPublic boards, shown as published, kept out of the index5 boards, none shared
| Benchmark | UI-TARS 7B | GLM 4.5V | Leader |
|---|---|---|---|
| AA Math Index | Not measured | 73.0 | Only GLM 4.5V measured |
| AIME 2025 (Artificial Analysis run) | Not measured | 73.0% | Only GLM 4.5V measured |
| LMArena Text: math | Not measured | 1354 | Only GLM 4.5V measured |
| LMArena Text: mathematical work | Not measured | 1336 | Only GLM 4.5V measured |
| MathArena | Not measured | 10.5% | Only GLM 4.5V measured |
| Neither model has a result on the other 9 boards here | |||
KnowledgeDirectional onlyPublic boards, shown as published, kept out of the index5 boards, none shared
| Benchmark | UI-TARS 7B | GLM 4.5V | Leader |
|---|---|---|---|
| MMLU Pro (Artificial Analysis run) | Not measured | 78.8% | Only GLM 4.5V measured |
| LMArena Text: science | Not measured | 1330 | Only GLM 4.5V measured |
| LMArena Text: medicine and healthcare | Not measured | 1349 | Only GLM 4.5V measured |
| LMArena Text: legal and government | Not measured | 1338 | Only GLM 4.5V measured |
| LMArena Text: business and finance | Not measured | 1309 | Only GLM 4.5V measured |
| Neither model has a result on the other 15 boards here | |||
Web designNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | UI-TARS 7B | GLM 4.5V | Leader |
|---|---|---|---|
| Neither model has a result on 9 boards here | |||
GeneralDirectional onlyPublic boards, shown as published, kept out of the index18 boards, none shared
| Benchmark | UI-TARS 7B | GLM 4.5V | Leader |
|---|---|---|---|
| IFBench | Not measured | 34.2% | Only GLM 4.5V measured |
| Long context reasoning (AA-LCR) | Not measured | 0.0% | Only GLM 4.5V measured |
| LMArena Text | Not measured | 1333 | Only GLM 4.5V measured |
| LMArena Text: instruction following | Not measured | 1311 | Only GLM 4.5V measured |
| LMArena Text: creative writing | Not measured | 1294 | Only GLM 4.5V measured |
| LMArena Vision | Not measured | 1154 | Only GLM 4.5V measured |
| LMArena Text: English | Not measured | 1362 | Only GLM 4.5V measured |
| LMArena Text: non-English | Not measured | 1303 | Only GLM 4.5V measured |
| LMArena Text: Chinese | Not measured | 1337 | Only GLM 4.5V measured |
| LMArena Text: Spanish | Not measured | 1336 | Only GLM 4.5V measured |
| LMArena Text: Russian | Not measured | 1298 | Only GLM 4.5V measured |
| LMArena Text: Polish | Not measured | 1302 | Only GLM 4.5V measured |
| LMArena Text: multi-turn | Not measured | 1333 | Only GLM 4.5V measured |
| LMArena Text: longer queries | Not measured | 1305 | Only GLM 4.5V measured |
| LMArena Text: writing and language | Not measured | 1302 | Only GLM 4.5V measured |
| LMArena Text: entertainment and media | Not measured | 1303 | Only GLM 4.5V measured |
| LMArena Vision: English | Not measured | 1183 | Only GLM 4.5V measured |
| LMArena Vision: reading text | Not measured | 1151 | Only GLM 4.5V measured |
| Neither model has a result on the other 30 boards here | |||
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | UI-TARS 7B | GLM 4.5V | 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 | Z.ai GLM 4.5V |
|---|---|---|
| Maker | ByteDance | Z.ai |
| Weights | Open weights | Open weights |
| Released | July 22, 2025 | August 11, 2025 |
| Context window | 128K | 66K |
| Input $/Mtok | $0.10 | $0.60 |
| Output $/Mtok | $0.20 | $1.80 |
| 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 GLM 4.5V for coding?
Neither model has a coding score yet, so the two cannot be ranked on coding. Pick UI-TARS 7B for output tokens 9.0x cheaper ($0.20 vs $1.80 per million) and a larger context window (128K against 66K tokens).
Which is cheaper, UI-TARS 7B or GLM 4.5V?
UI-TARS 7B costs $0.10 per million input tokens and $0.20 per million output tokens; GLM 4.5V costs $0.60 and $1.80. Cost per solved task is known for neither of them.
Which has the bigger context window, UI-TARS 7B or GLM 4.5V?
UI-TARS 7B, with 128K tokens against 66K.
How many benchmarks is the UI-TARS 7B vs GLM 4.5V 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 GLM 4.5V's on 0. Where only one model has a result, the page says so instead of naming a winner.