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Discover how Google's Gemini Flash and Mistral's Mistral Large stack up against each other in this comprehensive comparison of two leading AI
language models.
Released in May 2023 and February 2024 respectively, these models represent significant advancements in artificial intelligence,
with Gemini Flash offering a 1,000,000-token context
window and Mistral Large featuring a 32,000-token
capacity. Their distinct approaches to natural language processing are reflected in their
benchmark performances, with Gemini Flash achieving 78.9% on MMLU and Mistral Large scoring 81.2%, making this comparison essential
for developers and organizations seeking the right AI solution for their specific needs.
Models Overview
 Gemini Flash |  Mistral Large | |
|---|---|---|
Provider Company that developed the model | Mistral | |
Context Length Maximum number of tokens the model can process | 1M | 32K |
Maximum Output Maximum number of tokens the model can generate in a single response | 8192 | 4096 |
Release Date Date when the model was released | 14-05-2023 | 26-02-2024 |
Knowledge Cutoff Training data cutoff date | November 2023 | Unknown |
Open Source Whether the model's code is open-source | FALSE | TRUE |
API Providers API providers that offer access to the model | Vertex AI | Azure AI, AWS Bedrock, Google Cloud Vertex AI Model Garden, Snowflake Cortex, Hugging Face |
Pricing Comparison
Compare the pricing of Google's Gemini Flash and Mistral's Mistral Large to determine the most cost-effective solution for your AI needs.
| Gemini Flash | Mistral Large | |
|---|---|---|
Input Cost Cost per million input tokens | $0.13 / 1M tokens | $8 / 1M tokens |
Output Cost Cost per million tokens generated | $0.38 / 1M tokens | $8 / 1M tokens |
Comparing Benchmarks and Performance
Compare the performances of Google's Gemini Flash and Mistral's Mistral Large on industry benchmarks. This section provides a detailed comparison on MMLU, MMMU, HumanEval, MATH and other key benchmarks.
| Gemini Flash | Mistral Large | |
|---|---|---|
MMLU Evaluating LLM knowledge acquisition in zero-shot and few-shot settings. | 78.9% | 81.2% |
MMMU A wide ranging multi-discipline and multimodal benchmark. | 56.1% | Benchmark not available |
HellaSwag A challenging sentence completion benchmark. | 86.5% | 89.2% |
GSM8K Grade-school math problems benchmark. | 86.2% | 81% |
HumanEval A benchmark to measure functional correctness for synthesizing programs from docstrings. | 74.3% | 45.1% |
MATH Benchmark performance on Math problems ranging across 5 levels of difficulty and 7 sub-disciplines. | 54.9% | 45% |
