Model Comparison
o3-mini
vs. Qwen3.6 27B (Reasoning)
Comparing 2 AI models · 11 benchmarks · OpenAI, Alibaba
Recommended Pick
AlQwen3.6 27B (Reasoning)10 metric winsStrongest on: Value, Input price, Output price
Best Value
Qwen3.6 27B (Reasoning)
100.0 value score
47.6 reasoning / $1.35/1M
Lowest Price
Qwen3.6 27B (Reasoning)
$0.60/1M input price
Best Reasoning
o3-mini
55.4 reasoning score
Blends available reasoning benchmarks
Best for Coding
Qwen3.6 27B (Reasoning)
53.7 coding index
Composite Indices
Higher is better; speed and price are normalized
Standard Benchmarks
Only benchmarks with data are shown
Differences That Matter
Best value
Qwen3.6 27B (Reasoning) has the strongest quality-to-price mix at 100.0 out of 100 value points.
Price gap
Qwen3.6 27B (Reasoning) is 1.8x cheaper on input tokens than o3-mini.
Speed gap
o3-mini generates about 3.8x as many tokens per second as Qwen3.6 27B (Reasoning).
Reasoning gap
o3-mini leads Qwen3.6 27B (Reasoning) by 7.7 points on reasoning.
Top-pick rationale
Qwen3.6 27B (Reasoning) wins 10 measurable categories, including Value, Input price, Output price, Blended price.
Response Face-Off
Run one prompt through the selected models and compare response quality with live speed and cost context.
o3-mini
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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Qwen3.6 27B (Reasoning)
Alibaba
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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Which answer was more useful?
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Full Comparison
| Metric | Op o3-mini | Top Pick Al Qwen3.6 27B (Reasoning) |
|---|---|---|
| Pricing per 1M tokens | ||
| Input Cost | $1.10/1M | $0.60/1M |
| Output Cost | $4.40/1M | $3.60/1M |
| Blended (3:1) | $1.93/1M | $1.35/1M |
| Specifications | ||
| Organization | OpenAI | Alibaba |
| Release Date | Jan 31, 2025 | Apr 22, 2026 |
| Performance & Speed | ||
| Throughput | 231.8 tok/s | 61.0 tok/s |
| TTFT | 5220ms | 1534ms |
| Latency | 5220ms | 94511ms |
| Composite Indices | ||
| Value Score | 81.5 | 100.0 |
| Reasoning Score | 55.4 | 47.6 |
| Intelligence | 19.0 | 37.1 |
| Coding | — | 53.7 |
| Standard Benchmarks | ||
| GPQA | 74.8% | 84.2% |
| MMLU Pro | 79.1% | — |
| HLE | 8.7% | 21.6% |
| LiveCodeBench | 71.7% | — |
| MATH 500 | 97.3% | — |
| AIME (Original) | 77.0% | — |
| SciCode | 39.9% | 39.8% |
| LCR | — | 68.7% |
| IFBench | — | 67.6% |
| TAU-bench v2 | 28.7% | 94.2% |
| TerminalBench Hard | 6.8% | 34.8% |
Key Takeaways
Qwen3.6 27B (Reasoning) offers the best value at $0.60/1M,making it ideal for high-volume applications and cost-conscious projects.
o3-mini has the strongest reasoning profile with a 55.4 reasoning score,combining the available reasoning-heavy benchmarks.
Qwen3.6 27B (Reasoning) reaches a 53.7 coding index,making it the top choice for software development and code generation tasks.
All models support context windows of ∞+ tokens,suitable for processing lengthy documents and maintaining extended conversations.
When to Choose Each Model
o3-mini
- Complex reasoning tasks
- Research & analysis
Qwen3.6 27B (Reasoning)
- Cost-sensitive applications
- High-volume processing
- Code generation
- Software development
