Battle of the Models

Compare specific LLM models, context windows, and capabilities.

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Mistral Nemo Instruct 2407

A-TIER

LLM7.io

Intelligence Score 80/100
Model Popularity 0 votes
Context Window 128K Context
Pricing Model Free / Open
View Provider Analysis →

Qwen3.6 35B A3B

Hetzner Inference API

Intelligence Score 65/100
Context Window 262K
Pricing Model Free / Open
Model Popularity 0 votes
View Provider Analysis →
FINAL VERDICT

Mistral Nemo Instruct 2407 Wins

With an intelligence score of 80/100 vs 65/100, Mistral Nemo Instruct 2407 outperforms Qwen3.6 35B A3B by 15 points.

Clear Winner: Significant performance advantage for Mistral Nemo Instruct 2407.
HEAD-TO-HEAD

Detailed Comparison

Feature
Mistral Nemo Instruct 2407
Qwen3.6 35B A3B
Context Window
128K Context 262K
Architecture
Transformer (Open Weight) Transformer (Open Weight)
Est. MMLU Score
~75-79% ~60-64%
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) 10 requests, 4M input / 100K output tokens per 60s (per API key)
Daily Limit
100,000 tokens per 24h (free token; may be reduced without notice) No daily cap published (free while the API is experimental)
Capabilities
No specific data
Vision Reasoning
Performance Tier
B-Tier (Strong) C-Tier (Good)
Speed Estimate
Medium Medium
Primary Use Case
General Purpose General Purpose
Model Size
Undisclosed 35B
Limitations
  • No signup tier is heavily rate-limited (30 RPM)
  • Smaller, less well-known provider — verify uptime before production use
  • Experimental — no SLA, could be discontinued or paywalled at any time
  • Only one model currently offered
  • No official uptime/support guarantees
Key Strengths
  • Works with zero signup
  • OpenAI-compatible endpoint — swap base_url + key
  • No signup required for basic use
  • OpenAI-compatible endpoint — swap base_url + key
  • EU data residency
  • MoE model with 262K context and image input

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