Mistral 8x7B Instruct vs Qwen1.5-110B-Chat: Benchmarks, Pricing, and Context Window Comparison
Mistral 8x7B Instruct vs Qwen1.5-110B-Chat compares provider, context window, token pricing, benchmark performance, and release timeline in one side-by-side view. Use this page to quickly identify which model is a better fit for your production constraints, quality targets, and estimated cost per request.
Verdict
Mistral 8x7B Instruct has lower listed token pricing, while Qwen1.5-110B-Chat can still be preferable if benchmark results better match your workload.
Author: Mirai Minds Research Team
Last updated:
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Overview
Mistral 8x7B Instruct was released 5 months after Qwen1.5-110B-Chat.
Provider The entity that provides this model. | ||
Input Context Window The number of tokens supported by the input context window. | 32K tokens | 6,000 tokens |
Maximum Output Tokens The number of tokens that can be generated by the model in a single request. | 4,096 tokens | 2,000 tokens |
Release Date When the model was first released. | Dec 11, 2023 over 1 yearago 2023-12-11 | Aug 03, 2023 over 1 year 2023-08-03 |
Leaderboard
Rank | Unknown | Unknown |
Arena Elo | Not specified. | Not specified. |
95% CI | Not specified. | Not specified. |
Votes | Not specified. | Not specified. |
License | Not specified. | Not specified. |
Knowledge Cutoff | Unknown | Unknown |
Pricing
Input Cost of input data provided to the model. | $0.70 per million tokens | $6.375 per million tokens |
Output Cost of output tokens generated by the model. | $0.70 per million tokens | $2.125 per million tokens |
Benchmarks
Compare relevant benchmarks between Mistral 8x7B Instruct and Qwen1.5-110B-Chat Instruct.
MMLU Evaluating LLM knowledge acquisition in zero-shot and few-shot settings. | 70.6 (5-shot) | Benchmark not available. |
MMMU A wide ranging multi-discipline and multimodal benchmark. | Benchmark not available. | Benchmark not available. |
HellaSwag A challenging sentence completion benchmark. | Benchmark not available. | Benchmark not available. |
