Compare

Mistral-7b-Instruct-V0.2-Lora vs Mistral-Small-3.1-24b-Instruct

Pricing per million tokens, context window, capabilities — pulled from each provider's public docs. All 2 are available via the same AIgateway OpenAI-compatible endpoint; flip the model string to switch.

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Mistral-7b-Instruct-V0.2-Lora
mistral/mistral-7b-instruct-v0.2-lora
Mistral-Small-3.1-24b-Instruct
mistralai/mistral-small-3.1-24b-instruct
Provider
Mistral
Mistral
Family
Mistral
Mistral
Modality
text
text
Context window
4,096 tok
131,072 tok
Max output
4,096 tok
4,096 tok
Released
2024-04-01
2025-03-18
License
Open-weight
Open-weight
Input price
$0.050 /1M
$0.350 /1M
Output price
$0.100 /1M
$0.550 /1M
Tools
yes
Streaming
yes
yes
Vision
yes
JSON mode
yes
Reasoning
Prompt caching
Batch API
Try it
Open in playground →
Open in playground →
Mistral-7b-Instruct-V0.2-Lora
mistral/mistral-7b-instruct-v0.2-lora
Full spec →

The Mistral-7B-Instruct-v0.2 Large Language Model (LLM) is an instruct fine-tuned version of the Mistral-7B-v0.2.

Strengths
  • General-purpose chat
  • Long context
  • Tool use
Use cases
ChatbotsContent generationAgentic workflows
Mistral-Small-3.1-24b-Instruct
mistralai/mistral-small-3.1-24b-instruct
Full spec →

Building upon Mistral Small 3 (2501), Mistral Small 3.1 (2503) adds state-of-the-art vision understanding and enhances long context capabilities up to 128k tokens without compromising text performance. With 24 billion parameters, this model achieves top-tier capabilities in both text and vision tasks.

Strengths
  • Best cost/quality open model in its size class
  • Vision + tools
  • 128K context
Use cases
General chatMulti-modalCost-sensitive agents
SWITCH BETWEEN THEM

One key, all 2, one line different.

from openai import OpenAI

client = OpenAI(
    base_url="https://api.aigateway.sh/v1",
    api_key="sk-aig-...",
)

# Mistral-7b-Instruct-V0.2-Lora
client.chat.completions.create(
    model="mistral/mistral-7b-instruct-v0.2-lora",
    messages=[{"role":"user","content":"hello"}],
)

# Mistral-Small-3.1-24b-Instruct
client.chat.completions.create(
    model="mistralai/mistral-small-3.1-24b-instruct",
    messages=[{"role":"user","content":"hello"}],
)
Get an AIgateway keyRun an eval on these →