Model Comparison
o4-mini (high)
vs. Qwen3 VL 235B A22B Instruct
Comparing 2 AI models · 12 benchmarks · OpenAI, Alibaba
Recommended Pick
Opo4-mini (high)14 metric winsStrongest on: Throughput, Reasoning, Intelligence
Best Value
Qwen3 VL 235B A22B Instruct
100.0 value score
46.6 reasoning / $1.22/1M
Lowest Price
Qwen3 VL 235B A22B Instruct
$0.70/1M input price
Best Reasoning
o4-mini (high)
70.8 reasoning score
Blends available reasoning benchmarks
Composite Indices
Higher is better; speed and price are normalized
Standard Benchmarks
Only benchmarks with data are shown
Differences That Matter
Best value
Qwen3 VL 235B A22B Instruct has the strongest quality-to-price mix at 100.0 out of 100 value points.
Price gap
Qwen3 VL 235B A22B Instruct is 1.6x cheaper on input tokens than o4-mini (high).
Speed gap
o4-mini (high) generates about 3.3x as many tokens per second as Qwen3 VL 235B A22B Instruct.
Reasoning gap
o4-mini (high) leads Qwen3 VL 235B A22B Instruct by 24.2 points on reasoning.
Top-pick rationale
o4-mini (high) wins 14 measurable categories, including Throughput, Reasoning, Intelligence, Math.
Response Face-Off
Run one prompt through the selected models and compare response quality with live speed and cost context.
o4-mini (high)
OpenAI
TTFT
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Time
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tok/s
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Tokens
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Cost
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Qwen3 VL 235B A22B Instruct
Alibaba
TTFT
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Time
—
tok/s
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Tokens
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Cost
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Which answer was more useful?
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Full Comparison
| Metric | Top Pick Op o4-mini (high) | Al Qwen3 VL 235B A22B Instruct |
|---|---|---|
| Pricing per 1M tokens | ||
| Input Cost | $1.10/1M | $0.70/1M |
| Output Cost | $4.40/1M | $2.80/1M |
| Blended (3:1) | $1.93/1M | $1.22/1M |
| Specifications | ||
| Organization | OpenAI | Alibaba |
| Release Date | Apr 16, 2025 | Sep 23, 2025 |
| Performance & Speed | ||
| Throughput | 187.3 tok/s | 56.6 tok/s |
| TTFT | 18672ms | 1100ms |
| Latency | 18672ms | 1100ms |
| Composite Indices | ||
| Value Score | 96.6 | 100.0 |
| Reasoning Score | 70.8 | 46.6 |
| Intelligence | 25.6 | 14.3 |
| Math | 90.7 | 70.7 |
| Standard Benchmarks | ||
| GPQA | 78.4% | 71.2% |
| MMLU Pro | 83.2% | 82.3% |
| HLE | 17.5% | 6.3% |
| LiveCodeBench | 85.9% | 59.4% |
| MATH 500 | 98.9% | — |
| AIME 2025 | 90.7% | 70.7% |
| AIME (Original) | 94.0% | — |
| SciCode | 46.5% | 35.9% |
| LCR | 55.0% | 31.7% |
| IFBench | 68.7% | 42.7% |
| TAU-bench v2 | 55.6% | 35.1% |
| TerminalBench Hard | 15.2% | 6.8% |
Key Takeaways
Qwen3 VL 235B A22B Instruct offers the best value at $0.70/1M,making it ideal for high-volume applications and cost-conscious projects.
o4-mini (high) has the strongest reasoning profile with a 70.8 reasoning score,combining the available reasoning-heavy benchmarks.
All models support context windows of ∞+ tokens,suitable for processing lengthy documents and maintaining extended conversations.
When to Choose Each Model
o4-mini (high)
- Complex reasoning tasks
- Research & analysis
Qwen3 VL 235B A22B Instruct
- Cost-sensitive applications
- High-volume processing
