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

Mistral Large 2 (Jul '24)vsQwen3 Max (Preview)

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 Max (Preview)

Input
$1.2/M
Output
$6/M
Speed
58 tok/s
TTFT
4.19s

Winner by Category

Cheaper
Qwen3 Max (Preview)
Faster (tok/s)
Qwen3 Max (Preview)
Lower Latency
Qwen3 Max (Preview)
Benchmarks (0-1)
Qwen3 Max (Preview)

Pricing Comparison

MetricMistral Large 2 (Jul '24)Qwen3 Max (Preview)
Input ($/M tokens)$2$1.2
Output ($/M tokens)$6$6
Cost for 1M input + 100K output tokens:
Mistral Large 2 (Jul '24)$2.60
Qwen3 Max (Preview)$1.80

Speed Comparison

Output Speed (tokens/s) — higher is better
Mistral Large 2 (Jul '24)
Qwen3 Max (Preview)
58 tok/s
Time to First Token (seconds) — lower is better
Mistral Large 2 (Jul '24)
Qwen3 Max (Preview)
4.19s

Editorial Analysis

Verdict. Qwen3 Max (Preview) takes the aggregate benchmark matchup 1–0 across 1 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 Max (Preview) is meaningfully cheaper if your traffic is output-heavy (long completions, document generation, agent loops). Qwen3 Max (Preview) 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 Max (Preview) leads on Intelligence Index (19.4).

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 Max (Preview) costs $126.00 ($1512/year). At a smaller 5M-input/2M-output scale (single-developer tool or prototype): Mistral Large 2 (Jul '24) ≈ $22.00/run, Qwen3 Max (Preview) ≈ $18.00/run. At agent/realtime scale (200M input / 100M output per million requests): Mistral Large 2 (Jul '24) ≈ $1000/run, Qwen3 Max (Preview) ≈ $840/run. Qwen3 Max (Preview) 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
7.019.4
Coding Index
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
1 winsQwen3 Max (Preview)

Frequently Asked Questions

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

Qwen3 Max (Preview) is cheaper overall. Its blended price (3:1 input/output ratio) is $2.40/M tokens vs $3.00/M for Mistral Large 2 (Jul '24).

Which model performs better on benchmarks?

Qwen3 Max (Preview) wins 1 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 Max (Preview) generates tokens faster at 58 tok/s vs — tok/s. However, Qwen3 Max (Preview) has lower time-to-first-token (4.19s vs —s).

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

Choose based on your priorities: Qwen3 Max (Preview) for lower cost, Qwen3 Max (Preview) for stronger benchmark performance, and Qwen3 Max (Preview) for faster generation. For latency-sensitive apps, check the TTFT comparison above.