Model Comparison
Qwen2.5 Max
vs. 105B (high)
Comparing 2 AI models · 11 benchmarks · Alibaba, Sarvam
Recommended Pick
AlQwen2.5 Max6 metric winsStrongest on: Input price, Output price, TTFT
Lowest Price
Qwen2.5 Max
$0.00/1M input price
Best Reasoning
Qwen2.5 Max
36.0 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
Price gap
Qwen2.5 Max is ∞x cheaper on input tokens than 105B (high).
Reasoning gap
Qwen2.5 Max leads 105B (high) by 4.1 points on reasoning.
Top-pick rationale
Qwen2.5 Max wins 6 measurable categories, including Input price, Output price, TTFT, Latency.
Response Face-Off
Run one prompt through the selected models and compare response quality with live speed and cost context.
Qwen2.5 Max
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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105B (high)
Sarvam
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 Al Qwen2.5 Max | Sa 105B (high) |
|---|---|---|
| Pricing per 1M tokens | ||
| Input Cost | $0.00/1M | $0.04/1M |
| Output Cost | $0.00/1M | $0.17/1M |
| Blended (3:1) | — | $0.07/1M |
| Specifications | ||
| Organization | Alibaba | Sarvam |
| Release Date | Jan 28, 2025 | Mar 6, 2026 |
| Performance & Speed | ||
| Throughput | — | 142.3 tok/s |
| TTFT | — | 1168ms |
| Latency | — | 15224ms |
| Composite Indices | ||
| Value Score | — | 100.0 |
| Reasoning Score | 36.0 | 31.9 |
| Intelligence | 10.2 | 11.9 |
| Standard Benchmarks | ||
| GPQA | 58.7% | 73.8% |
| MMLU Pro | 76.2% | — |
| HLE | 4.5% | 10.1% |
| LiveCodeBench | 35.9% | — |
| MATH 500 | 83.5% | — |
| AIME (Original) | 23.3% | — |
| SciCode | 33.7% | 26.4% |
| LCR | — | 0.0% |
| IFBench | — | 34.4% |
| TAU-bench v2 | — | 46.8% |
| TerminalBench Hard | — | 1.5% |
Key Takeaways
Qwen2.5 Max offers the best value at $0.00/1M,making it ideal for high-volume applications and cost-conscious projects.
Qwen2.5 Max has the strongest reasoning profile with a 36.0 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
Qwen2.5 Max
- Cost-sensitive applications
- High-volume processing
- Complex reasoning tasks
- Research & analysis
105B (high)
- General-purpose AI
- Versatile applications
