Model Comparison
Llama 3.2 90B Instruct vs Qwen3 VL 4B ThinkingWhich is better in 2026?
Both models are evenly matched across the benchmarks. Qwen3 VL 4B Thinking is 1.1x cheaper per token.
Verdict: Llama 3.2 90B Instruct vs Qwen3 VL 4B Thinking — which is better?
Llama 3.2 90B Instruct (by Meta) and Qwen3 VL 4B Thinking (by Alibaba Cloud / Qwen Team) are two of the AI models people compare most. Here is how they stack up on benchmarks, price and capabilities, and which one to pick in 2026.
Llama 3.2 90B Instruct outperforms in 2 benchmarks (AI2D, MMLU), while Qwen3 VL 4B Thinking is better at 2 benchmarks (GPQA, MMMU-Pro). Both models are evenly matched across the benchmarks.
On price, Qwen3 VL 4B Thinking is roughly 1.1x cheaper per token on a blended 3:1 input/output basis, which adds up quickly at production volume.
Qwen3 VL 4B Thinking also accepts a larger context window (262,144 input tokens), making it the stronger choice for long documents and large codebases.
Choose Llama 3.2 90B Instruct if…
- you want predictable pricing at $0.35/M input and $0.40/M output
Choose Qwen3 VL 4B Thinking if…
- cost matters — it's about 1.1x cheaper per token
- you process long inputs — it offers a 262,144 token context window
- you want the most recent training data — it shipped Sep 2025
Performance Benchmarks
Comparative analysis across standard metrics
Llama 3.2 90B Instruct outperforms in 2 benchmarks (AI2D, MMLU), while Qwen3 VL 4B Thinking is better at 2 benchmarks (GPQA, MMMU-Pro).
Both models are evenly matched across the benchmarks.
Arena Performance
Human preference votes
Pricing Analysis
Price comparison per million tokens
For input processing, Llama 3.2 90B Instruct ($0.35/1M tokens) is 3.5x more expensive than Qwen3 VL 4B Thinking ($0.10/1M tokens).
For output processing, Llama 3.2 90B Instruct ($0.40/1M tokens) is 2.5x cheaper than Qwen3 VL 4B Thinking ($1.00/1M tokens).
In conclusion, Llama 3.2 90B Instruct is more expensive than Qwen3 VL 4B Thinking.*
* Using a 3:1 ratio of input to output tokens
Model Size
Parameter count comparison
Llama 3.2 90B Instruct has 86.0B more parameters than Qwen3 VL 4B Thinking, making it 2150.0% larger.
Context Window
Maximum input and output token capacity
Qwen3 VL 4B Thinking accepts 262,144 input tokens compared to Llama 3.2 90B Instruct's 128,000 tokens. Qwen3 VL 4B Thinking can generate longer responses up to 262,144 tokens, while Llama 3.2 90B Instruct is limited to 128,000 tokens.
Input Capabilities
Supported data types and modalities
Both Llama 3.2 90B Instruct and Qwen3 VL 4B Thinking support multimodal inputs.
They are both capable of processing various types of data, offering versatility in application.
Llama 3.2 90B Instruct
Qwen3 VL 4B Thinking
License
Usage and distribution terms
Llama 3.2 90B Instruct is licensed under Llama 3.2, while Qwen3 VL 4B Thinking uses Apache 2.0.
License differences may affect how you can use these models in commercial or open-source projects.
Llama 3.2
Open weights
Apache 2.0
Open weights
Release Timeline
When each model was launched
Llama 3.2 90B Instruct was released on 2024-09-25, while Qwen3 VL 4B Thinking was released on 2025-09-22.
Qwen3 VL 4B Thinking is 12 months newer than Llama 3.2 90B Instruct.
Sep 25, 2024
1.7 years ago
Sep 22, 2025
8 months ago
12mo newerKnowledge Cutoff
When training data ends
Neither model specifies a knowledge cutoff date.
Unable to compare the recency of their training data.
Provider Availability
Llama 3.2 90B Instruct is available from DeepInfra, Bedrock, Fireworks, Together, Hyperbolic. Qwen3 VL 4B Thinking is available from DeepInfra.
Llama 3.2 90B Instruct
Qwen3 VL 4B Thinking
Outputs Comparison
Key Takeaways
Qwen3 VL 4B Thinking
View detailsAlibaba Cloud / Qwen Team
Detailed Comparison
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FAQ
Common questions about Llama 3.2 90B Instruct vs Qwen3 VL 4B Thinking.