Compare/Step 3.5 Flash 2603 vs Mistral Small 3.1

Step 3.5 Flash 2603vsMistral Small 3.1

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

StepFun

Step 3.5 Flash 2603

Input
$0.1/M
Output
$0.3/M
Speed
214 tok/s
TTFT
1.14s
Mistral

Mistral Small 3.1

Input
$0.1/M
Output
$0.3/M
Speed
149 tok/s
TTFT
0.81s

Winner by Category

Cheaper
Tie
Faster (tok/s)
Step 3.5 Flash 2603
Lower Latency
Mistral Small 3.1
Benchmarks (1-1)
Tie

Pricing Comparison

MetricStep 3.5 Flash 2603Mistral 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 2603$0.13
Mistral Small 3.1$0.13

Speed Comparison

Output Speed (tokens/s) — higher is better
Step 3.5 Flash 2603
214 tok/s
Mistral Small 3.1
149 tok/s
Time to First Token (seconds) — lower is better
Step 3.5 Flash 2603
1.14s
Mistral Small 3.1
0.81s

Editorial Analysis

Verdict. Step 3.5 Flash 2603 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 2603 is strongest on Intelligence Index (26.5). Mistral Small 3.1 leads on Coding Index (26.3), Intelligence Index (14.9).

Speed. On throughput, Step 3.5 Flash 2603 generates tokens at 214 tok/s versus 149 tok/s — about 30% faster. On time-to-first-token, Mistral Small 3.1 responds in 810ms vs 1140ms, 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 2603 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 2603 ≈ $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 2603 ≈ $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.

Benchmark Comparison

Data from Artificial Analysis API — 12 benchmarks

Intelligence Index
26.514.9
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 Flash 26031 wins
1 winsMistral Small 3.1

Frequently Asked Questions

Which is cheaper, Step 3.5 Flash 2603 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 2603 generates tokens faster at 214 tok/s vs 149 tok/s. However, Mistral Small 3.1 has lower time-to-first-token (0.81s vs 1.14s).

When should I use Step 3.5 Flash 2603 vs Mistral Small 3.1?

Choose based on your priorities: both are similarly priced, both perform similarly on benchmarks, and Step 3.5 Flash 2603 for faster generation. For latency-sensitive apps, check the TTFT comparison above.