Which model is better: Mistral Large 2 (2411) or Qwen 3.8 Flash Next?
Qwen 3.8 Flash Next is 92.0% cheaper for input tokens ($0.16 vs. $2.00 per 1M tokens) and $0.47 vs. $6.00 for output tokens (12.5x cost advantage). In terms of operational performance, Qwen 3.8 Flash Next delivers lower response latency at 280ms TTFT (100ms faster than Mistral Large 2 (2411)) with 185 vs. 75 tok/s throughput. Qwen 3.8 Flash Next leads coding evaluations at 81.0% SWE-bench Verified vs. 45.2% for Mistral Large 2 (2411) and 91.7% vs. 54.2% on GPQA Diamond.
Verified daily via automated API latency tests and official documentation.
Zero-Shot RAG Verified
Verified Performance & Unit Economics Delta (12 Dimensions)
Daily synchronized metrics across token pricing, prompt cache economics, latency, throughput, and 5 capability benchmarks.
Metric / Dimension
Mistral Large 2 (2411)
Qwen 3.8 Flash Next
Net Delta / Advantage
Input Cost / 1M Tokens
$2.00
$0.16
Qwen 3.8 Flash Next is 12.5x cheaper
Output Cost / 1M Tokens
$6.00
$0.47
Qwen 3.8 Flash Next (92.2% lower)
Prompt Cache Read / 1M
$0.200
$0.016
Qwen 3.8 Flash Next is 12.5x cheaper
Prompt Cache Write / 1M
$2.00
$0.20
Qwen 3.8 Flash Next is 10.0x cheaper
Context Window (Max Output)
131k (8k out)
268k (66k out)
Qwen 3.8 Flash Next (2.0x larger context)
Avg TTFT Response Latency
380ms
280ms
Qwen 3.8 Flash Next (100ms faster)
Generation Speed (Throughput)
75 tok/s
185 tok/s
Qwen 3.8 Flash Next (+110 tok/s faster)
SWE-bench Verified (Coding)
45.2%
81%
Qwen 3.8 Flash Next (+35.8% lead)
GPQA Diamond (PhD STEM)
54.2%
91.7%
Qwen 3.8 Flash Next (+37.5% lead)
MMLU-Pro (Multi-Discipline)
74.8%
88.4%
Qwen 3.8 Flash Next (+13.6% lead)
MATH-500 (Quantitative Logic)
76.5%
95.7%
Qwen 3.8 Flash Next (+19.2% lead)
Inference Value Score (IVR)
50.1 / 100
85.1 / 100
Qwen 3.8 Flash Next (+35 pts)
Interactive Monthly Token Economics & ROI Forecaster
Model production workloads across prompt, completion, and cached context tokens, or switch models on the fly.
Choose Mistral Large 2 (2411) (Mistral AI) for sovereign enterprise multilingual flagship tuned for reasoning, strict JSON schemas, and European regulatory compliance — offering a 131k context window, 380ms TTFT, and $2.00/1M input pricing ($0.200/1M cached reads).
Which is cheaper: Mistral Large 2 (2411) or Qwen 3.8 Flash Next?
Qwen 3.8 Flash Next is cheaper at $0.16/1M input tokens ($0.47/1M output) compared to $2.00/1M input ($6.00/1M output) for Mistral Large 2 (2411).
Which model has lower TTFT latency and higher tok/s throughput: Mistral Large 2 (2411) or Qwen 3.8 Flash Next?
Qwen 3.8 Flash Next delivers faster first-token latency at 280ms TTFT (185 tok/s throughput) compared to 380ms TTFT (75 tok/s) for Mistral Large 2 (2411).
How do Mistral Large 2 (2411) and Qwen 3.8 Flash Next compare on SWE-bench coding and GPQA Diamond reasoning?
On SWE-bench Verified software engineering, Mistral Large 2 (2411) scores 45.2% versus 81% for Qwen 3.8 Flash Next. On GPQA Diamond graduate-level STEM reasoning, Mistral Large 2 (2411) scores 54.2% versus 91.7% for Qwen 3.8 Flash Next (and 76.5% vs 95.7% on MATH-500).
When should you choose Mistral Large 2 (2411)?
Choose Mistral Large 2 (2411) (Mistral AI) for sovereign enterprise multilingual flagship tuned for reasoning, strict JSON schemas, and European regulatory compliance — offering a 131k context window, 380ms TTFT, and $2.00/1M input pricing ($0.200/1M cached reads).
When should you choose Qwen 3.8 Flash Next?
Choose Qwen 3.8 Flash Next (Alibaba Cloud) for next-generation open-weight multimodal MoE activating only 6B parameters per token for ultra-low-cost enterprise office automation, tool calling, and multilingual repository engineering — offering a 268k context window, 280ms TTFT, and $0.16/1M input pricing ($0.016/1M cached reads).
