Compare/Qwen3 VL 8B Instruct vs Mixtral 8x7B Instruct

Qwen3 VL 8B InstructvsMixtral 8x7B Instruct

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

Alibaba

Qwen3 VL 8B Instruct

Input
$0.18/M
Output
$0.7/M
Speed
107 tok/s
TTFT
2.26s
Mistral

Mixtral 8x7B Instruct

Input
$0.45/M
Output
$0.7/M
Speed
TTFT

Winner by Category

Cheaper
Qwen3 VL 8B Instruct
Faster (tok/s)
Qwen3 VL 8B Instruct
Lower Latency
Qwen3 VL 8B Instruct
Benchmarks (1-0)
Qwen3 VL 8B Instruct

Pricing Comparison

MetricQwen3 VL 8B InstructMixtral 8x7B Instruct
Input ($/M tokens)$0.18$0.45
Output ($/M tokens)$0.7$0.7
Cost for 1M input + 100K output tokens:
Qwen3 VL 8B Instruct$0.25
Mixtral 8x7B Instruct$0.52

Speed Comparison

Output Speed (tokens/s) — higher is better
Qwen3 VL 8B Instruct
107 tok/s
Mixtral 8x7B Instruct
Time to First Token (seconds) — lower is better
Qwen3 VL 8B Instruct
2.26s
Mixtral 8x7B Instruct

Editorial Analysis

Verdict. Qwen3 VL 8B Instruct wins the overall benchmark matchup 1–0 across 1 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, Mixtral 8x7B Instruct is meaningfully cheaper if your traffic is output-heavy (long completions, document generation, agent loops). Mixtral 8x7B Instruct makes more sense when output volume is low and absolute reasoning quality justifies the premium.

Strengths. Qwen3 VL 8B Instruct is strongest on Intelligence Index (8.2). Mixtral 8x7B Instruct leads on Intelligence Index (2.0).

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

Provider. Alibaba and Mistral sell to overlapping but distinct developer audiences: Alibaba 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): Qwen3 VL 8B Instruct costs $15.90 ($191/year); Mixtral 8x7B Instruct costs $24.00 ($288/year). At a smaller 5M-input/2M-output scale (single-developer tool or prototype): Qwen3 VL 8B Instruct ≈ $2.30/run, Mixtral 8x7B Instruct ≈ $3.65/run. At agent/realtime scale (200M input / 100M output per million requests): Qwen3 VL 8B Instruct ≈ $106/run, Mixtral 8x7B Instruct ≈ $160/run. Qwen3 VL 8B Instruct 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
8.22.0
Coding Index
Math Index
GPQA Diamond
MMLU-Pro
LiveCodeBench
AIME 2025
MATH-500
Humanity's Last Exam
SciCode
IFBench
TerminalBench
Qwen3 VL 8B Instruct1 wins
0 winsMixtral 8x7B Instruct

Frequently Asked Questions

Which is cheaper, Qwen3 VL 8B Instruct or Mixtral 8x7B Instruct?

Qwen3 VL 8B Instruct is cheaper overall. Its blended price (3:1 input/output ratio) is $0.31/M tokens vs $0.51/M for Mixtral 8x7B Instruct.

Which model performs better on benchmarks?

Qwen3 VL 8B Instruct wins 1 out of 12 benchmarks compared to 0 for Mixtral 8x7B Instruct. See the detailed benchmark chart above for per-category results.

Which is faster for real-time applications?

Qwen3 VL 8B Instruct generates tokens faster at 107 tok/s vs — tok/s. Qwen3 VL 8B Instruct also has lower time-to-first-token (2.26s vs —s).

When should I use Qwen3 VL 8B Instruct vs Mixtral 8x7B Instruct?

Choose based on your priorities: Qwen3 VL 8B Instruct for lower cost, Qwen3 VL 8B Instruct for stronger benchmark performance, and Qwen3 VL 8B Instruct for faster generation. For latency-sensitive apps, check the TTFT comparison above.