Model Comparison
DeepSeek-V4-Flash-0731 vs Llama 3.2 90B InstructWhich is better in 2026?
Comparing DeepSeek-V4-Flash-0731 and Llama 3.2 90B Instruct across benchmarks, pricing, and capabilities.
Verdict: DeepSeek-V4-Flash-0731 vs Llama 3.2 90B Instruct — which is better?
DeepSeek-V4-Flash-0731 (by DeepSeek) and Llama 3.2 90B Instruct (by Meta) 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.
On price, DeepSeek-V4-Flash-0731 is roughly 3.2x cheaper per token on a blended 3:1 input/output basis, which adds up quickly at production volume.
DeepSeek-V4-Flash-0731 also accepts a larger context window (1,048,576 input tokens), making it the stronger choice for long documents and large codebases.
Choose DeepSeek-V4-Flash-0731 if…
- cost matters — it's about 3.2x cheaper per token
- you process long inputs — it offers a 1,048,576 token context window
- you want the most recent training data — it shipped Jul 2026
Choose Llama 3.2 90B Instruct if…
- you want predictable pricing at $0.35/M input and $0.40/M output
Performance Benchmarks
Comparative analysis across standard metrics
DeepSeek-V4-Flash-0731 and Llama 3.2 90B Instructdon't have any common benchmark datasets to compare. They may have been evaluated on different testing suites.
Arena Performance
Human preference votes
Pricing Analysis
Price comparison per million tokens
For input processing, DeepSeek-V4-Flash-0731 ($0.09/1M tokens) is 3.9x cheaper than Llama 3.2 90B Instruct ($0.35/1M tokens).
For output processing, DeepSeek-V4-Flash-0731 ($0.18/1M tokens) is 2.2x cheaper than Llama 3.2 90B Instruct ($0.40/1M tokens).
In conclusion, Llama 3.2 90B Instruct is more expensive than DeepSeek-V4-Flash-0731.*
* Using a 3:1 ratio of input to output tokens
Model Size
Parameter count comparison
DeepSeek-V4-Flash-0731 has 214.0B more parameters than Llama 3.2 90B Instruct, making it 237.8% larger.
Context Window
Maximum input and output token capacity
DeepSeek-V4-Flash-0731 accepts 1,048,576 input tokens compared to Llama 3.2 90B Instruct's 128,000 tokens. Llama 3.2 90B Instruct can generate longer responses up to 128,000 tokens, while DeepSeek-V4-Flash-0731 is limited to 65,536 tokens.
Input Capabilities
Supported data types and modalities
Llama 3.2 90B Instruct supports multimodal inputs, whereas DeepSeek-V4-Flash-0731 does not.
Llama 3.2 90B Instruct can handle both text and other forms of data like images, making it suitable for multimodal applications.
DeepSeek-V4-Flash-0731
Llama 3.2 90B Instruct
License
Usage and distribution terms
DeepSeek-V4-Flash-0731 is licensed under MIT, while Llama 3.2 90B Instruct uses Llama 3.2.
License differences may affect how you can use these models in commercial or open-source projects.
MIT
Open weights
Llama 3.2
Open weights
Release Timeline
When each model was launched
DeepSeek-V4-Flash-0731 was released on 2026-07-31, while Llama 3.2 90B Instruct was released on 2024-09-25.
DeepSeek-V4-Flash-0731 is 22 months newer than Llama 3.2 90B Instruct.
Jul 31, 2026
3 days ago
1.8yr newerSep 25, 2024
1.9 years ago
Knowledge Cutoff
When training data ends
Neither model specifies a knowledge cutoff date.
Unable to compare the recency of their training data.
Provider Availability
DeepSeek-V4-Flash-0731 is available from DeepInfra, Fireworks, Novita. Llama 3.2 90B Instruct is available from DeepInfra, Bedrock, Fireworks, Together, Hyperbolic.
DeepSeek-V4-Flash-0731
Llama 3.2 90B Instruct
Outputs Comparison
Key Takeaways
Detailed Comparison
Interactive Arena
Judge for yourself.
Run your own prompts against DeepSeek-V4-Flash-0731 and Llama 3.2 90B Instruct side-by-side, then vote on the output you prefer.
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FAQ
Common questions about DeepSeek-V4-Flash-0731 vs Llama 3.2 90B Instruct.