Compare/Magistral Small 1.2 vs Qwen3.7 Plus

Magistral Small 1.2vsQwen3.7 Plus

Side-by-side comparison of pricing, 12 benchmarks, and generation speed.

Mistral

Magistral Small 1.2

Input
$0.5/M
Output
$1.5/M
Speed
TTFT
Alibaba

Qwen3.7 Plus

Input
$0.4/M
Output
$1.6/M
Speed
57 tok/s
TTFT
2.23s

Winner by Category

Cheaper
Qwen3.7 Plus
Faster (tok/s)
Qwen3.7 Plus
Lower Latency
Qwen3.7 Plus
Benchmarks (0-2)
Qwen3.7 Plus

Pricing Comparison

MetricMagistral Small 1.2Qwen3.7 Plus
Input ($/M tokens)$0.5$0.4
Output ($/M tokens)$1.5$1.6
Cost for 1M input + 100K output tokens:
Magistral Small 1.2$0.65
Qwen3.7 Plus$0.56

Speed Comparison

Output Speed (tokens/s) — higher is better
Magistral Small 1.2
Qwen3.7 Plus
57 tok/s
Time to First Token (seconds) — lower is better
Magistral Small 1.2
Qwen3.7 Plus
2.23s

Editorial Analysis

Verdict. Qwen3.7 Plus takes the aggregate benchmark matchup 2–0 across 2 categories. Real workloads usually care about a handful of specific tasks — see the per-benchmark table above.

Pricing. Both models sit in the budget bracket for output-token pricing. At 0.9× the per-million-token cost, Magistral Small 1.2 is meaningfully cheaper if your traffic is output-heavy (long completions, document generation, agent loops). Magistral Small 1.2 makes more sense when output volume is low and absolute reasoning quality justifies the premium.

Strengths. Magistral Small 1.2 is strongest on Coding Index (14.7), Intelligence Index (11.5). Qwen3.7 Plus leads on Coding Index (55.9), Intelligence Index (39.4).

Speed. Speed data is incomplete for this pair; benchmark and price should decide.

Provider. Mistral and Alibaba sell to overlapping but distinct developer audiences: Mistral tends to ship frontier reasoning models with premium positioning, while Alibaba often prices more aggressively. Your existing vendor relationships, billing, and SLA preferences may matter as much as the raw numbers above.

Workload cost. Workload scenarios (per million requests at 30M input + 15M output tokens): Magistral Small 1.2 costs $37.50 ($450/year); Qwen3.7 Plus costs $36.00 ($432/year). At a smaller 5M-input/2M-output scale (single-developer tool or prototype): Magistral Small 1.2 ≈ $5.50/run, Qwen3.7 Plus ≈ $5.20/run. At agent/realtime scale (200M input / 100M output per million requests): Magistral Small 1.2 ≈ $250/run, Qwen3.7 Plus ≈ $240/run. Qwen3.7 Plus becomes more attractive at higher volume — the absolute per-token pricing difference compounds when you ship at scale.

Recommendation. Both models have legitimate use cases — the right answer depends on whether you are optimizing for benchmark ceiling, latency, or unit cost. Start with the cheaper / faster model, evaluate against your specific task, and only switch if the upgrade shows a meaningful lift.

Benchmark Comparison

Data from Artificial Analysis API — 12 benchmarks

Intelligence Index
11.539.4
Coding Index
14.755.9
Math Index
GPQA Diamond
MMLU-Pro
LiveCodeBench
AIME 2025
MATH-500
Humanity's Last Exam
SciCode
IFBench
TerminalBench
Magistral Small 1.20 wins
2 winsQwen3.7 Plus

Frequently Asked Questions

Which is cheaper, Magistral Small 1.2 or Qwen3.7 Plus?

Qwen3.7 Plus is cheaper overall. Its blended price (3:1 input/output ratio) is $0.70/M tokens vs $0.75/M for Magistral Small 1.2.

Which model performs better on benchmarks?

Qwen3.7 Plus wins 2 out of 12 benchmarks compared to 0 for Magistral Small 1.2. See the detailed benchmark chart above for per-category results.

Which is faster for real-time applications?

Qwen3.7 Plus generates tokens faster at 57 tok/s vs — tok/s. However, Qwen3.7 Plus has lower time-to-first-token (2.23s vs —s).

When should I use Magistral Small 1.2 vs Qwen3.7 Plus?

Choose based on your priorities: Qwen3.7 Plus for lower cost, Qwen3.7 Plus for stronger benchmark performance, and Qwen3.7 Plus for faster generation. For latency-sensitive apps, check the TTFT comparison above.