Compare/Mistral Large 2 (Jul '24) vs Qwen3.8 Max

Mistral Large 2 (Jul '24)vsQwen3.8 Max

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

Mistral

Mistral Large 2 (Jul '24)

Input
$2/M
Output
$6/M
Speed
TTFT
Alibaba

Qwen3.8 Max

Input
$2/M
Output
$6/M
Speed
50 tok/s
TTFT
2.56s

Winner by Category

Cheaper
Tie
Faster (tok/s)
Qwen3.8 Max
Lower Latency
Qwen3.8 Max
Benchmarks (0-2)
Qwen3.8 Max

Pricing Comparison

MetricMistral Large 2 (Jul '24)Qwen3.8 Max
Input ($/M tokens)$2$2
Output ($/M tokens)$6$6
Cost for 1M input + 100K output tokens:
Mistral Large 2 (Jul '24)$2.60
Qwen3.8 Max$2.60

Speed Comparison

Output Speed (tokens/s) — higher is better
Mistral Large 2 (Jul '24)
Qwen3.8 Max
50 tok/s
Time to First Token (seconds) — lower is better
Mistral Large 2 (Jul '24)
Qwen3.8 Max
2.56s

Editorial Analysis

Verdict. Qwen3.8 Max takes the aggregate benchmark matchup 2–0 across 2 categories. Real workloads usually care about a handful of specific tasks — see the per-benchmark table above.

Pricing. Both models sit in the mid-tier bracket for output-token pricing. At 1.0× the per-million-token cost, Qwen3.8 Max is meaningfully cheaper if your traffic is output-heavy (long completions, document generation, agent loops). Qwen3.8 Max makes more sense when output volume is low and absolute reasoning quality justifies the premium.

Strengths. Mistral Large 2 (Jul '24) is strongest on Intelligence Index (7.0). Qwen3.8 Max leads on Coding Index (71.8), Intelligence Index (58.1).

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

Provider. Mistral and Alibaba sell to overlapping but distinct developer audiences: Mistral tends to ship frontier reasoning models with premium positioning, while Alibaba 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 Large 2 (Jul '24) costs $150.00 ($1800/year); Qwen3.8 Max costs $150.00 ($1800/year). At a smaller 5M-input/2M-output scale (single-developer tool or prototype): Mistral Large 2 (Jul '24) ≈ $22.00/run, Qwen3.8 Max ≈ $22.00/run. At agent/realtime scale (200M input / 100M output per million requests): Mistral Large 2 (Jul '24) ≈ $1000/run, Qwen3.8 Max ≈ $1000/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.

Benchmark Comparison

Data from Artificial Analysis API — 12 benchmarks

Intelligence Index
7.058.1
Coding Index
71.8
Math Index
GPQA Diamond
MMLU-Pro
LiveCodeBench
AIME 2025
MATH-500
Humanity's Last Exam
SciCode
IFBench
TerminalBench
Mistral Large 2 (Jul '24)0 wins
2 winsQwen3.8 Max

Frequently Asked Questions

Which is cheaper, Mistral Large 2 (Jul '24) or Qwen3.8 Max?

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

Which model performs better on benchmarks?

Qwen3.8 Max wins 2 out of 12 benchmarks compared to 0 for Mistral Large 2 (Jul '24). See the detailed benchmark chart above for per-category results.

Which is faster for real-time applications?

Qwen3.8 Max generates tokens faster at 50 tok/s vs — tok/s. However, Qwen3.8 Max has lower time-to-first-token (2.56s vs —s).

When should I use Mistral Large 2 (Jul '24) vs Qwen3.8 Max?

Choose based on your priorities: both are similarly priced, Qwen3.8 Max for stronger benchmark performance, and Qwen3.8 Max for faster generation. For latency-sensitive apps, check the TTFT comparison above.