Compare/Step 3.5 Flash vs Mistral Small 3.1

Step 3.5 FlashvsMistral Small 3.1

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

StepFun

Step 3.5 Flash

Input
$0.1/M
Output
$0.3/M
Speed
214 tok/s
TTFT
1.05s
Mistral

Mistral Small 3.1

Input
$0.1/M
Output
$0.3/M
Speed
138 tok/s
TTFT
0.88s

Winner by Category

Cheaper
Tie
Faster (tok/s)
Step 3.5 Flash
Lower Latency
Mistral Small 3.1
Benchmarks (1-1)
Tie

Pricing Comparison

MetricStep 3.5 FlashMistral Small 3.1
Input ($/M tokens)$0.1$0.1
Output ($/M tokens)$0.3$0.3
Cost for 1M input + 100K output tokens:
Step 3.5 Flash$0.13
Mistral Small 3.1$0.13

Speed Comparison

Output Speed (tokens/s) — higher is better
Step 3.5 Flash
214 tok/s
Mistral Small 3.1
138 tok/s
Time to First Token (seconds) — lower is better
Step 3.5 Flash
1.05s
Mistral Small 3.1
0.88s

Editorial Analysis

Verdict. Step 3.5 Flash and Mistral Small 3.1 split the benchmark comparison evenly at 1–1. The tiebreaker here is price, speed, and what you actually run them on.

Pricing. Both models sit in the budget bracket for output-token pricing. At 1.0× the per-million-token cost, Mistral Small 3.1 is meaningfully cheaper if your traffic is output-heavy (long completions, document generation, agent loops). Mistral Small 3.1 makes more sense when output volume is low and absolute reasoning quality justifies the premium.

Strengths. Step 3.5 Flash is strongest on Intelligence Index (26.0). Mistral Small 3.1 leads on Coding Index (26.3), Intelligence Index (14.9).

Speed. On throughput, Step 3.5 Flash generates tokens at 214 tok/s versus 138 tok/s — about 36% faster. On time-to-first-token, Mistral Small 3.1 responds in 880ms vs 1050ms, which matters most for chat-style UIs.

Provider. StepFun and Mistral sell to overlapping but distinct developer audiences: StepFun tends to ship frontier reasoning models with premium positioning, while Mistral 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): Step 3.5 Flash costs $7.50 ($90/year); Mistral Small 3.1 costs $7.50 ($90/year). At a smaller 5M-input/2M-output scale (single-developer tool or prototype): Step 3.5 Flash ≈ $1.10/run, Mistral Small 3.1 ≈ $1.10/run. At agent/realtime scale (200M input / 100M output per million requests): Step 3.5 Flash ≈ $50/run, Mistral Small 3.1 ≈ $50/run.

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.

Head-to-head deltas

  • Benchmark wins tie exactly at 1–1. The tiebreaker on raw benchmark parity will be price, speed, or capability coverage.
  • On throughput, Step 3.5 Flash is 1.55× faster (214 tok/s vs 138 tok/s). For streaming chat or real-time agents this alone often flips the recommendation.

Benchmark Comparison

Data from Artificial Analysis API — 12 benchmarks

Intelligence Index
26.014.9
Coding Index
26.3
Math Index
GPQA Diamond
MMLU-Pro
LiveCodeBench
AIME 2025
MATH-500
Humanity's Last Exam
SciCode
IFBench
TerminalBench
Step 3.5 Flash1 wins
1 winsMistral Small 3.1

Frequently Asked Questions

Which is cheaper, Step 3.5 Flash or Mistral Small 3.1?

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

Which model performs better on benchmarks?

It's a tie — both models win 1 benchmarks each across 12 evaluated categories. See the detailed benchmark chart above for per-category results.

Which is faster for real-time applications?

Step 3.5 Flash generates tokens faster at 214 tok/s vs 138 tok/s. However, Mistral Small 3.1 has lower time-to-first-token (0.88s vs 1.05s).

When should I use Step 3.5 Flash vs Mistral Small 3.1?

Choose based on your priorities: both are similarly priced, both perform similarly on benchmarks, and Step 3.5 Flash for faster generation. For latency-sensitive apps, check the TTFT comparison above.