Compare/Mistral Small 3.1 vs Ling 2.6 Flash

Mistral Small 3.1vsLing 2.6 Flash

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

Mistral

Mistral Small 3.1

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

Ling 2.6 Flash

Input
$0.1/M
Output
$0.3/M
Speed
104 tok/s
TTFT
1.13s

Winner by Category

Cheaper
Tie
Faster (tok/s)
Mistral Small 3.1
Lower Latency
Mistral Small 3.1
Benchmarks (2-0)
Mistral Small 3.1

Pricing Comparison

MetricMistral Small 3.1Ling 2.6 Flash
Input ($/M tokens)$0.1$0.1
Output ($/M tokens)$0.3$0.3
Cost for 1M input + 100K output tokens:
Mistral Small 3.1$0.13
Ling 2.6 Flash$0.13

Speed Comparison

Output Speed (tokens/s) — higher is better
Mistral Small 3.1
149 tok/s
Ling 2.6 Flash
104 tok/s
Time to First Token (seconds) — lower is better
Mistral Small 3.1
0.81s
Ling 2.6 Flash
1.13s

Editorial Analysis

Verdict. Mistral Small 3.1 wins the overall benchmark matchup 2–0 across 2 overlapping categories, but raw benchmark score is only one input to the decision.

Pricing. Both models sit in the budget bracket for output-token pricing. At 1.0× the per-million-token cost, Ling 2.6 Flash is meaningfully cheaper if your traffic is output-heavy (long completions, document generation, agent loops). Ling 2.6 Flash makes more sense when output volume is low and absolute reasoning quality justifies the premium.

Strengths. Mistral Small 3.1 is strongest on Coding Index (26.3), Intelligence Index (14.9). Ling 2.6 Flash leads on Coding Index (25.3), Intelligence Index (14.2).

Speed. On throughput, Mistral Small 3.1 generates tokens at 149 tok/s versus 104 tok/s — about 30% faster. On time-to-first-token, Mistral Small 3.1 responds in 810ms vs 1130ms, which matters most for chat-style UIs.

Provider. Mistral and InclusionAI sell to overlapping but distinct developer audiences: Mistral tends to ship frontier reasoning models with premium positioning, while InclusionAI 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): Mistral Small 3.1 costs $7.50 ($90/year); Ling 2.6 Flash costs $7.50 ($90/year). At a smaller 5M-input/2M-output scale (single-developer tool or prototype): Mistral Small 3.1 ≈ $1.10/run, Ling 2.6 Flash ≈ $1.10/run. At agent/realtime scale (200M input / 100M output per million requests): Mistral Small 3.1 ≈ $50/run, Ling 2.6 Flash ≈ $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

  • Aggregate benchmark score (sum across 12 categories, capped at 100): Mistral Small 3.1 = 41, Ling 2.6 Flash = 40. Within 15% — effectively equivalent if both meet the threshold your product requires.

Benchmark Comparison

Data from Artificial Analysis API — 12 benchmarks

Intelligence Index
14.914.2
Coding Index
26.325.3
Math Index
GPQA Diamond
MMLU-Pro
LiveCodeBench
AIME 2025
MATH-500
Humanity's Last Exam
SciCode
IFBench
TerminalBench
Mistral Small 3.12 wins
0 winsLing 2.6 Flash

Frequently Asked Questions

Which is cheaper, Mistral Small 3.1 or Ling 2.6 Flash?

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

Which model performs better on benchmarks?

Mistral Small 3.1 wins 2 out of 12 benchmarks compared to 0 for Ling 2.6 Flash. See the detailed benchmark chart above for per-category results.

Which is faster for real-time applications?

Mistral Small 3.1 generates tokens faster at 149 tok/s vs 104 tok/s. Mistral Small 3.1 also has lower time-to-first-token (0.81s vs 1.13s).

When should I use Mistral Small 3.1 vs Ling 2.6 Flash?

Choose based on your priorities: both are similarly priced, Mistral Small 3.1 for stronger benchmark performance, and Mistral Small 3.1 for faster generation. For latency-sensitive apps, check the TTFT comparison above.