Which model is better: Mistral Large 2 (2411) or Qwen 3.8 27B?
Qwen 3.8 27B is 90.0% cheaper for input tokens ($0.20 vs. $2.00 per 1M tokens) and $0.60 vs. $6.00 for output tokens (10.0x cost advantage). In terms of operational performance, Qwen 3.8 27B delivers lower response latency at 120ms TTFT (260ms faster than Mistral Large 2 (2411)) with 140 vs. 75 tok/s throughput. Qwen 3.8 27B leads coding evaluations at 58.4% SWE-bench Verified vs. 45.2% for Mistral Large 2 (2411) and 68.0% 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 27B
Net Delta / Advantage
Input Cost / 1M Tokens
$2.00
$0.20
Qwen 3.8 27B is 10.0x cheaper
Output Cost / 1M Tokens
$6.00
$0.60
Qwen 3.8 27B (90.0% lower)
Prompt Cache Read / 1M
$0.200
$0.050
Qwen 3.8 27B is 4.0x cheaper
Prompt Cache Write / 1M
$2.00
$0.20
Qwen 3.8 27B is 10.0x cheaper
Context Window (Max Output)
131k (8k out)
131k (16k out)
Equal Context Window
Avg TTFT Response Latency
380ms
120ms
Qwen 3.8 27B (260ms faster)
Generation Speed (Throughput)
75 tok/s
140 tok/s
Qwen 3.8 27B (+65 tok/s faster)
SWE-bench Verified (Coding)
45.2%
58.4%
Qwen 3.8 27B (+13.2% lead)
GPQA Diamond (PhD STEM)
54.2%
68%
Qwen 3.8 27B (+13.8% lead)
MMLU-Pro (Multi-Discipline)
74.8%
81.2%
Qwen 3.8 27B (+6.4% lead)
MATH-500 (Quantitative Logic)
76.5%
86.4%
Qwen 3.8 27B (+9.9% lead)
Inference Value Score (IVR)
50.1 / 100
74.9 / 100
Qwen 3.8 27B (+24.8 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).
Choose Qwen 3.8 27B (Alibaba Cloud) for high-performance 27B open weights optimized for local deployment, code generation, and low-latency production pipelines — offering a 131k context window, 120ms TTFT, and $0.20/1M input pricing ($0.050/1M cached reads).
Which is cheaper: Mistral Large 2 (2411) or Qwen 3.8 27B?
Qwen 3.8 27B is cheaper at $0.20/1M input tokens ($0.60/1M 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 27B?
Qwen 3.8 27B delivers faster first-token latency at 120ms TTFT (140 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 27B compare on SWE-bench coding and GPQA Diamond reasoning?
On SWE-bench Verified software engineering, Mistral Large 2 (2411) scores 45.2% versus 58.4% for Qwen 3.8 27B. On GPQA Diamond graduate-level STEM reasoning, Mistral Large 2 (2411) scores 54.2% versus 68% for Qwen 3.8 27B (and 76.5% vs 86.4% 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 27B?
Choose Qwen 3.8 27B (Alibaba Cloud) for high-performance 27B open weights optimized for local deployment, code generation, and low-latency production pipelines — offering a 131k context window, 120ms TTFT, and $0.20/1M input pricing ($0.050/1M cached reads).
