Battle of the Models

Compare specific LLM models, context windows, and capabilities.

Mistral Nemo Instruct 2407

A-TIER

LLM7.io

Intelligence Score 80/100

Model Popularity 0 votes

Context Window 128K Context

Pricing Model Free / Open

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A-TIER

Requesty

Intelligence Score 83/100

Context Window 262K Context

Pricing Model Free / Open

Model Popularity 0 votes

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FINAL VERDICT

With an intelligence score of 83/100 vs 80/100, inclusionAI Ling 3.1 Flash outperforms Mistral Nemo Instruct 2407 by 3 points.

Close Match: The difference is minimal. Consider other factors like pricing and features.

HEAD-TO-HEAD

Feature	Mistral Nemo Instruct 2407	inclusionAI Ling 3.1 Flash
Context Window	128K Context	262K Context
Architecture	Transformer (Open Weight)	Transformer
Est. MMLU Score	~75-79%	~75-79%
Release Date	2024	2024
Pricing Model	Free Tier	Free Tier
Rate Limit (RPM)	1 req/s, 60/min, 250/hour (free token from dash.llm7.io)	20 RPM on free models (60 RPM for paying orgs)
Daily Limit	100,000 tokens per 24h (free token; may be reduced without notice)	200 requests/day (new orgs) / 1,000/day (paying orgs), shared across free models
Capabilities	No specific data	No specific data
Performance Tier	B-Tier (Strong)	B-Tier (Strong)
Speed Estimate	Medium	⚡ Very Fast
Primary Use Case	General Purpose	⚡ Fast Chat & Apps
Model Size	Undisclosed	Undisclosed
Limitations	No signup tier is heavily rate-limited (30 RPM) Smaller, less well-known provider — verify uptime before production use	Requires underlying provider API keys Free credit amount is limited Routing adds minimal latency
Key Strengths	Works with zero signup OpenAI-compatible endpoint — swap base_url + key No signup required for basic use	AI Router: automatic provider failover Prompt caching for cost savings Multi-provider load balancing