Battle of the Models

Compare specific LLM models, context windows, and capabilities.

Qwen3.6 35B A3B

Hetzner Inference API

Intelligence Score 65/100

Model Popularity 0 votes

Context Window 262K

Pricing Model Free / Open

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A-TIER

LLM7.io

Intelligence Score 80/100

Context Window 128K Context

Pricing Model Free / Open

Model Popularity 0 votes

View Provider Analysis →

FINAL VERDICT

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

Feature	Qwen3.6 35B A3B	Mistral Nemo Instruct 2407
Context Window	262K	128K Context
Architecture	Transformer (Open Weight)	Transformer (Open Weight)
Est. MMLU Score	~60-64%	~75-79%
Release Date	2024	2024
Pricing Model	Free Tier	Free Tier
Rate Limit (RPM)	3M input / 60K output tokens per 60s	30 RPM (no signup) / 120 RPM (free email token)
Daily Limit	500M input / 5M output tokens per 24h	Up to 5M tokens/day (rolling 24h, with free token)
Capabilities	Vision Reasoning	No specific data
Performance Tier	C-Tier (Good)	B-Tier (Strong)
Speed Estimate	Medium	Medium
Primary Use Case	General Purpose	General Purpose
Model Size	35B	Undisclosed
Limitations	Experimental — no SLA, could be discontinued or paywalled at any time Only one model currently offered No official uptime/support guarantees	No signup tier is heavily rate-limited (30 RPM) Smaller, less well-known provider — verify uptime before production use
Key Strengths	OpenAI-compatible endpoint — swap base_url + key EU data residency MoE model with 262K context and image input	Works with zero signup OpenAI-compatible endpoint — swap base_url + key No signup required for basic use