Compare/Gemini 3.1 Flash-Lite vs Magistral Small 1.2

Gemini 3.1 Flash-LitevsMagistral Small 1.2

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

Google

Gemini 3.1 Flash-Lite

Input
$0.25/M
Output
$1.5/M
Speed
346 tok/s
TTFT
5.64s
Mistral

Magistral Small 1.2

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

Winner by Category

Cheaper
Gemini 3.1 Flash-Lite
Faster (tok/s)
Gemini 3.1 Flash-Lite
Lower Latency
Gemini 3.1 Flash-Lite
Benchmarks (2-0)
Gemini 3.1 Flash-Lite

Pricing Comparison

MetricGemini 3.1 Flash-LiteMagistral Small 1.2
Input ($/M tokens)$0.25$0.5
Output ($/M tokens)$1.5$1.5
Cost for 1M input + 100K output tokens:
Gemini 3.1 Flash-Lite$0.40
Magistral Small 1.2$0.65

Speed Comparison

Output Speed (tokens/s) — higher is better
Gemini 3.1 Flash-Lite
346 tok/s
Magistral Small 1.2
Time to First Token (seconds) — lower is better
Gemini 3.1 Flash-Lite
5.64s
Magistral Small 1.2

Editorial Analysis

Verdict. Gemini 3.1 Flash-Lite 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, 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. Gemini 3.1 Flash-Lite is strongest on Coding Index (34.7), Intelligence Index (25.6). Magistral Small 1.2 leads on Coding Index (14.7), Intelligence Index (11.5).

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

Provider. Google and Mistral sell to overlapping but distinct developer audiences: Google 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): Gemini 3.1 Flash-Lite costs $30.00 ($360/year); Magistral Small 1.2 costs $37.50 ($450/year). At a smaller 5M-input/2M-output scale (single-developer tool or prototype): Gemini 3.1 Flash-Lite ≈ $4.25/run, Magistral Small 1.2 ≈ $5.50/run. At agent/realtime scale (200M input / 100M output per million requests): Gemini 3.1 Flash-Lite ≈ $200/run, Magistral Small 1.2 ≈ $250/run. Gemini 3.1 Flash-Lite 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
25.611.5
Coding Index
34.714.7
Math Index
GPQA Diamond
MMLU-Pro
LiveCodeBench
AIME 2025
MATH-500
Humanity's Last Exam
SciCode
IFBench
TerminalBench
Gemini 3.1 Flash-Lite2 wins
0 winsMagistral Small 1.2

Frequently Asked Questions

Which is cheaper, Gemini 3.1 Flash-Lite or Magistral Small 1.2?

Gemini 3.1 Flash-Lite is cheaper overall. Its blended price (3:1 input/output ratio) is $0.56/M tokens vs $0.75/M for Magistral Small 1.2.

Which model performs better on benchmarks?

Gemini 3.1 Flash-Lite 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?

Gemini 3.1 Flash-Lite generates tokens faster at 346 tok/s vs — tok/s. Gemini 3.1 Flash-Lite also has lower time-to-first-token (5.64s vs —s).

When should I use Gemini 3.1 Flash-Lite vs Magistral Small 1.2?

Choose based on your priorities: Gemini 3.1 Flash-Lite for lower cost, Gemini 3.1 Flash-Lite for stronger benchmark performance, and Gemini 3.1 Flash-Lite for faster generation. For latency-sensitive apps, check the TTFT comparison above.