Model Comparison
Qwen3 VL 4B Instruct
vs. M (Reasoning)
Comparing 2 AI models · 12 benchmarks · Alibaba, Sarvam
Recommended Pick
SaM (Reasoning)6 metric winsStrongest on: Reasoning, GPQA, MMLU Pro
Lowest Price
Qwen3 VL 4B Instruct
$0.00/1M input price
Best Reasoning
M (Reasoning)
30.5 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
Reasoning gap
M (Reasoning) leads Qwen3 VL 4B Instruct by 6.8 points on reasoning.
Top-pick rationale
M (Reasoning) wins 6 measurable categories, including Reasoning, GPQA, MMLU Pro, LiveCodeBench.
Response Face-Off
Run one prompt through the selected models and compare response quality with live speed and cost context.
Qwen3 VL 4B Instruct
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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M (Reasoning)
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 | Al Qwen3 VL 4B Instruct | Top Pick Sa M (Reasoning) |
|---|---|---|
| Pricing per 1M tokens | ||
| Input Cost | $0.00/1M | $0.00/1M |
| Output Cost | $0.00/1M | $0.00/1M |
| Specifications | ||
| Organization | Alibaba | Sarvam |
| Release Date | Oct 14, 2025 | May 23, 2025 |
| Performance & Speed | ||
| Throughput | — | — |
| TTFT | — | — |
| Latency | — | — |
| Composite Indices | ||
| Reasoning Score | 23.7 | 30.5 |
| Intelligence | 3.7 | 2.6 |
| Math | 37.0 | — |
| Standard Benchmarks | ||
| GPQA | 37.1% | 41.6% |
| MMLU Pro | 63.4% | 69.6% |
| HLE | 3.6% | 3.1% |
| LiveCodeBench | 29.0% | 29.5% |
| MATH 500 | — | 84.7% |
| AIME 2025 | 37.0% | — |
| AIME (Original) | — | 20.3% |
| SciCode | 13.7% | 17.8% |
| LCR | 14.3% | 0.0% |
| IFBench | 31.8% | 31.8% |
| TAU-bench v2 | 23.4% | 0.0% |
| TerminalBench Hard | 0.0% | 2.3% |
Key Takeaways
Qwen3 VL 4B Instruct offers the best value at $0.00/1M,making it ideal for high-volume applications and cost-conscious projects.
M (Reasoning) has the strongest reasoning profile with a 30.5 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
Qwen3 VL 4B Instruct
- Cost-sensitive applications
- High-volume processing
M (Reasoning)
- Complex reasoning tasks
- Research & analysis
