| Signal | Qwen3 235B A22B Instruct 2507 | Delta | UI-TARS 7B |
|---|---|---|---|
Capabilities | 67 | +33 | |
Pricing | 0 | 0 | |
Context window size | 86 | +5 | |
Recency | 86 | 0 | |
Output Capacity | 20 | -35 | |
| Overall Result | 2 wins | of 5 | 3 wins |
8
days higher
4
days
18
days higher
Alibaba
ByteDance
Qwen3 235B A22B Instruct 2507 saves you $7.90/month
That's $94.80/year compared to UI-TARS 7B at your current usage level of 100K calls/month.
| Metric | Qwen3 235B A22B Instruct 2507 | UI-TARS 7B | Winner |
|---|---|---|---|
| Overall Score | 40 | 40 | -- |
| Rank | #224 | #222 | UI-TARS 7B |
| Quality Rank | #224 | #222 | UI-TARS 7B |
| Adoption Rank | #224 | #222 | UI-TARS 7B |
| Parameters | 235B | 7B | -- |
| Context Window | 262K | 128K | Qwen3 235B A22B Instruct 2507 |
| Pricing | $0.07/$0.10/M | $0.10/$0.20/M | -- |
| Signal Scores | |||
| Capabilities | 67 | 33 | Qwen3 235B A22B Instruct 2507 |
| Pricing | 0 | 0 | UI-TARS 7B |
| Context window size | 86 | 81 | Qwen3 235B A22B Instruct 2507 |
| Recency | 86 | 87 | UI-TARS 7B |
| Output Capacity | 20 | 55 | UI-TARS 7B |
Our score (0-100) is driven by benchmark performance (90%) from Arena Elo ratings, MMLU, GPQA, HumanEval, SWE-bench, and 15+ standardized evaluations. Capabilities and context window serve as tiebreakers (10%). Here's what the scores mean for these two models:
Scores 40/100 (rank #224), placing it in the top 23% of all 290 models tracked.
Scores 40/100 (rank #222), placing it in the top 24% of all 290 models tracked.
With only a 0-point gap, these models are in the same performance tier. The practical difference in output quality is minimal - your choice should depend on pricing, latency requirements, and specific feature needs.
Qwen3 235B A22B Instruct 2507 offers 43% better value per quality point. At 1M tokens/day, you'd spend $2.56/month with Qwen3 235B A22B Instruct 2507 vs $4.50/month with UI-TARS 7B - a $1.93 monthly difference.
Both models have comparable response speeds. For most applications, the latency difference is negligible.
When latency matters most: Interactive chatbots, IDE code completion, real-time translation, and user-facing applications where response time directly impacts experience. For batch processing, background summarization, or offline analysis, latency is less critical.
Code generation & review
Higher benchmark score (0/100) indicates stronger performance on coding tasks like generating functions, debugging, and refactoring
Customer support chatbot
Faster response time (speed score 0/100) is critical for user-facing chat. Qwen3 235B A22B Instruct 2507 also offers lower per-token costs for high-volume support
Long document analysis
Larger context window (262K tokens) can process longer documents, contracts, and research papers in a single pass
Batch data extraction
Lower output pricing ($0.10/M) reduces costs when processing thousands of records daily
Creative writing & content
Higher overall composite score (40/100) correlates with better nuance, coherence, and style in long-form content
Image understanding & OCR
Supports vision input - can analyze screenshots, diagrams, photos, and scanned documents directly
Qwen3 235B A22B Instruct 2507 and UI-TARS 7B are extremely close in overall performance (only 0 points apart). Your best choice depends entirely on which specific strengths matter most for your use case.
Best for Quality
Qwen3 235B A22B Instruct 2507
Marginally better benchmark scores; both are excellent
Best for Cost
Qwen3 235B A22B Instruct 2507
43% lower pricing; better value at scale
Best for Reliability
Qwen3 235B A22B Instruct 2507
Higher uptime and faster response speeds
Best for Prototyping
Qwen3 235B A22B Instruct 2507
Stronger community support and better developer experience
Best for Production
Qwen3 235B A22B Instruct 2507
Wider enterprise adoption and proven at scale
by Alibaba
| Capability | Qwen3 235B A22B Instruct 2507 | UI-TARS 7B |
|---|---|---|
| Vision (Image Input)differs | ||
| Function Callingdiffers | ||
| Streaming | ||
| JSON Modediffers | ||
| Reasoningdiffers | ||
| Web Search | ||
| Image Output |
Alibaba
ByteDance
Qwen3 235B A22B Instruct 2507 saves you $0.1722/month
That's 41% cheaper than UI-TARS 7B at 1,000 tokens/request and 100 requests/day.
Assumes 60% input / 40% output token ratio per request. Actual costs may vary based on your usage pattern.
| Parameter | Qwen3 235B A22B Instruct 2507 | UI-TARS 7B |
|---|---|---|
| Context Window | 262K | 128K |
| Max Output Tokens | -- | 2,048 |
| Open Source | Yes | Yes |
| Created | Jul 21, 2025 | Jul 22, 2025 |
Both Qwen3 235B A22B Instruct 2507 and UI-TARS 7B score 40/100, making them extremely close competitors. Choose based on pricing, provider ecosystem, or specific capability requirements.
Qwen3 235B A22B Instruct 2507 is ranked #224 and UI-TARS 7B is ranked #222 out of 290+ AI models. Rankings use a composite score combining benchmark performance (90%) from MMLU, GPQA, HumanEval, SWE-bench, and 15+ standardized evaluations, with capabilities and context window as tiebreakers (10%). Scores update hourly.
Qwen3 235B A22B Instruct 2507 is cheaper at $0.10/M output tokens vs UI-TARS 7B 's $0.20/M output tokens - 2.0x more expensive. Input token pricing: Qwen3 235B A22B Instruct 2507 at $0.07/M vs UI-TARS 7B at $0.10/M.
Qwen3 235B A22B Instruct 2507 has a larger context window of 262,144 tokens compared to UI-TARS 7B 's 128,000 tokens. A larger context window means the model can process longer documents and conversations.