UI-TARS 7B vs GPT-5.1-Codex
UI-TARS 7B costs 50.0x less per output token than GPT-5.1-Codex ($0.20 vs $10.00 per million). Updated October 22, 2026; no benchmark has tested both models yet.
- Coding
- No data
- Overall
- No data
- Output $/M
- $0.20
- Coding
- 15.3
- Overall
- No data
- Output $/M
- $10.00
UI-TARS 7B or GPT-5.1-Codex: which to pick
Only GPT-5.1-Codex has a coding score, 15.3 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 50.0x cheaper ($0.20 vs $10.00 per million).
No benchmark has tested both yet. UI-TARS 7B has results on Kagi LLM Benchmark; GPT-5.1-Codex on SWE-bench Verified, LMArena WebDev 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 GPT-5.1-Codex has a coding score, so no call is made.
One side not measured
Intelligence
AA Intelligence Index
No call
Only GPT-5.1-Codex has a AA Intelligence Index result, 23.7.
Directional only
Web design
Design Arena: websites
No call
Only GPT-5.1-Codex has a Design Arena: websites result, 1161.
Directional only
Long documents
Listed context window
GPT-5.1-Codex
GPT-5.1-Codex takes 400K tokens against 128K.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
UI-TARS 7B
UI-TARS 7B $0.0002, GPT-5.1-Codex $0.0063 per turn.
List prices
Cost per solved task
One SWE-bench Verified task, solved
No call
Only GPT-5.1-Codex has the number, $0.892.
Directional only
At a glance
- Coding index
- UI-TARS 7B not benchmarked, GPT-5.1-Codex 15.3 from 2 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 GPT-5.1-Codex $10.00 per million output tokens, 50.0x apart.
- Context window
- UI-TARS 7B takes 128K tokens, GPT-5.1-Codex 400K.
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 Index9 boards measured
| Benchmark | UI-TARS 7B | GPT-5.1-Codex | Leader |
|---|---|---|---|
| SWE-bench Verified | Not measured | 66.0% | Only GPT-5.1-Codex measured |
| LMArena WebDev | Not measured | 1337 | Only GPT-5.1-Codex measured |
| LiveCodeBench (Vals AI) | Not measured | 85.5% | Only GPT-5.1-Codex measured |
| Vibe Code Bench (Vals AI) | Not measured | 13.1% | Only GPT-5.1-Codex measured |
| LiveCodeBench (Artificial Analysis run) | Not measured | 84.9% | Only GPT-5.1-Codex measured |
| Terminal-Bench Hard (Artificial Analysis run) | Not measured | 34.8% | Only GPT-5.1-Codex measured |
| Terminal-Bench | Not measured | 57.8% | Only GPT-5.1-Codex measured |
| ALE-Bench | Not measured | 1244.9 | Only GPT-5.1-Codex measured |
| LMArena WebDev: HTML | Not measured | 1346 | Only GPT-5.1-Codex measured |
| Neither model has a result on the other 30 boards here | |||
IntelligenceDirectional onlyPublic boards, shown as published, kept out of the index1 board measured
| Benchmark | UI-TARS 7B | GPT-5.1-Codex | Leader |
|---|---|---|---|
| AA Intelligence Index | Not measured | 23.7 | Only GPT-5.1-Codex measured |
| Neither model has a result on the other 1 board here | |||
Agentic workDirectional onlyPublic boards, shown as published, kept out of the index4 boards measured
| Benchmark | UI-TARS 7B | GPT-5.1-Codex | Leader |
|---|---|---|---|
| Design Arena: agents building web apps | Not measured | 1018 | Only GPT-5.1-Codex measured |
| Design Arena: agents building full-stack apps | Not measured | 1086 | Only GPT-5.1-Codex measured |
| Design Arena: agents building mobile apps | Not measured | 1191 | Only GPT-5.1-Codex measured |
| τ²-Bench Telecom | Not measured | 83.0% | Only GPT-5.1-Codex measured |
| Neither model has a result on the other 37 boards here | |||
ReasoningDirectional onlyPublic boards, shown as published, kept out of the index3 boards measured
| Benchmark | UI-TARS 7B | GPT-5.1-Codex | Leader |
|---|---|---|---|
| GPQA Diamond (Artificial Analysis run) | Not measured | 86.0% | Only GPT-5.1-Codex measured |
| Humanity's Last Exam (Artificial Analysis run) | Not measured | 25.7% | Only GPT-5.1-Codex measured |
| Kagi LLM Benchmark | 20.7% | Not measured | Only UI-TARS 7B measured |
| Neither model has a result on the other 19 boards here | |||
MathDirectional onlyPublic boards, shown as published, kept out of the index2 boards measured
| Benchmark | UI-TARS 7B | GPT-5.1-Codex | Leader |
|---|---|---|---|
| AA Math Index | Not measured | 95.7 | Only GPT-5.1-Codex measured |
| AIME 2025 (Artificial Analysis run) | Not measured | 95.7% | Only GPT-5.1-Codex measured |
| Neither model has a result on the other 11 boards here | |||
KnowledgeDirectional onlyPublic boards, shown as published, kept out of the index1 board measured
| Benchmark | UI-TARS 7B | GPT-5.1-Codex | Leader |
|---|---|---|---|
| MMLU Pro (Artificial Analysis run) | Not measured | 86.0% | Only GPT-5.1-Codex measured |
| Neither model has a result on the other 17 boards here | |||
Web designDirectional onlyPublic boards, shown as published, kept out of the index5 boards measured
| Benchmark | UI-TARS 7B | GPT-5.1-Codex | Leader |
|---|---|---|---|
| Design Arena: websites | Not measured | 1161 | Only GPT-5.1-Codex measured |
| Design Arena: UI components | Not measured | 1234 | Only GPT-5.1-Codex measured |
| Design Arena: game dev | Not measured | 1137 | Only GPT-5.1-Codex measured |
| Design Arena: data viz | Not measured | 1213 | Only GPT-5.1-Codex measured |
| Design Arena: all code categories | Not measured | 1152 | Only GPT-5.1-Codex measured |
| Neither model has a result on the other 4 boards here | |||
GeneralDirectional onlyPublic boards, shown as published, kept out of the index2 boards measured
| Benchmark | UI-TARS 7B | GPT-5.1-Codex | Leader |
|---|---|---|---|
| IFBench | Not measured | 70.0% | Only GPT-5.1-Codex measured |
| Long context reasoning (AA-LCR) | Not measured | 69.3% | Only GPT-5.1-Codex measured |
| Neither model has a result on the other 44 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 | OpenAI GPT-5.1-Codex |
|---|---|---|
| Maker | ByteDance | OpenAI |
| Weights | Open weights | No public weights listed |
| Released | July 22, 2025 | November 13, 2025 |
| Context window | 128K | 400K |
| Input $/Mtok | $0.10 | $1.25 |
| Output $/Mtok | $0.20 | $10.00 |
| Coding index | Not benchmarked | 15.3 |
| Independent measurements behind it | 0 | 2 |
| Cost per solved task | Not computable | $0.892 |
| Source | OpenRouter listing | OpenRouter listing |
Questions
Is UI-TARS 7B better than GPT-5.1-Codex for coding?
Only GPT-5.1-Codex has a coding score, 15.3 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 50.0x cheaper ($0.20 vs $10.00 per million).
Which is cheaper, UI-TARS 7B or GPT-5.1-Codex?
UI-TARS 7B costs $0.10 per million input tokens and $0.20 per million output tokens; GPT-5.1-Codex costs $1.25 and $10.00. Cost per solved task is known for GPT-5.1-Codex only.
Which has the bigger context window, UI-TARS 7B or GPT-5.1-Codex?
GPT-5.1-Codex, with 400K tokens against 128K.
Has any benchmark tested both UI-TARS 7B and GPT-5.1-Codex?
No benchmark has tested both yet. UI-TARS 7B has results on Kagi LLM Benchmark; GPT-5.1-Codex on SWE-bench Verified, LMArena WebDev and LiveCodeBench (Vals AI). Compare them on price and context instead. Data as of October 22, 2026.