Compare/Mistral Small 3.2 vs Step 3.5 Flash

Mistral Small 3.2vsStep 3.5 Flash

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

Mistral

Mistral Small 3.2

Input
$0.1/M
Output
$0.3/M
Speed
196 tok/s
TTFT
0.31s
StepFun

Step 3.5 Flash

Input
$0.1/M
Output
$0.3/M
Speed
93 tok/s
TTFT
3.48s

Winner by Category

Cheaper
Tie
Faster (tok/s)
Mistral Small 3.2
Lower Latency
Mistral Small 3.2
Benchmarks (5-7)
Step 3.5 Flash

Pricing Comparison

MetricMistral Small 3.2Step 3.5 Flash
Input ($/M tokens)$0.1$0.1
Output ($/M tokens)$0.3$0.3
Cost for 1M input + 100K output tokens:
Mistral Small 3.2$0.13
Step 3.5 Flash$0.13

Speed Comparison

Output Speed (tokens/s) — higher is better
Mistral Small 3.2
196 tok/s
Step 3.5 Flash
93 tok/s
Time to First Token (seconds) — lower is better
Mistral Small 3.2
0.31s
Step 3.5 Flash
3.48s

Benchmark Comparison

Data from Artificial Analysis API — 12 benchmarks

Intelligence Index
15.137.8
Coding Index
13.331.6
Math Index
27.0
GPQA Diamond
50.5%83.1%
MMLU-Pro
68.1%
LiveCodeBench
27.5%
AIME 2025
27.0%
MATH-500
88.3%
Humanity's Last Exam
4.3%19.1%
SciCode
26.4%40.4%
IFBench
33.5%64.6%
TerminalBench
6.8%27.3%
Mistral Small 3.25 wins
7 winsStep 3.5 Flash

Frequently Asked Questions

Which is cheaper, Mistral Small 3.2 or Step 3.5 Flash?

Both models have similar pricing. Check the detailed breakdown above for input vs output token costs.

Which model performs better on benchmarks?

Step 3.5 Flash wins 7 out of 12 benchmarks compared to 5 for Mistral Small 3.2. See the detailed benchmark chart above for per-category results.

Which is faster for real-time applications?

Mistral Small 3.2 generates tokens faster at 196 tok/s vs 93 tok/s. Mistral Small 3.2 also has lower time-to-first-token (0.31s vs 3.48s).

When should I use Mistral Small 3.2 vs Step 3.5 Flash?

Choose based on your priorities: both are similarly priced, Step 3.5 Flash for stronger benchmark performance, and Mistral Small 3.2 for faster generation. For latency-sensitive apps, check the TTFT comparison above.