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Article · 2026-07-31 · 4 moments

deepseek-ai/DeepSeek-V4-Flash-0731

deepseek-ai/DeepSeek-V4-Flash-0731 The latest release in DeepSeek's V4 family, "with substantially enhanced agentic capabilities". It's 304 billion parameters - 167GB on Hugging Face - but it appears to punch well above its weight. Artificial Analysis rank it ahead of MiniMax M3 - a 428B model. It's $0.14/million input and $0.27/million output pricing means this may currently be the best value-per-intelligence model out there. It's looking very good on the Intelligence Index vs. Cost per Intel ✦ AI generated

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Claim

At $0.14 per million input tokens and $0.27 per million output tokens, DeepSeek-V4-Flash-0731 may currently be the best value-per-intelligence model available, performing very well on the Intelligence Index versus cost.

Highlights the model's pricing — $0.14/M input, $0.27/M output — and argues it may currently be the best value-per-intelligence model, with a strong showing on the Intelligence Index vs. Cost per Intelligence Index Task chart.

transcript

Author: It's $0.14/million input and $0.27/million output pricing means this may currently be the best value-per-intelligence model out there. It's looking very good on the Intelligence Index vs. Cost per Intelligence Index Task chart:

supports · 2

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Context

DeepSeek-V4-Flash-0731, the latest release in DeepSeek's V4 family, has substantially enhanced agentic capabilities and, at 304 billion parameters (167GB on Hugging Face), punches well above its weight.

Introduces DeepSeek-V4-Flash-0731 as the newest V4-family release with 'substantially enhanced agentic capabilities', noting its 304B parameters (167GB on Hugging Face) and arguing it punches well above its weight.

transcript

Author: The latest release in DeepSeek's V4 family, "with substantially enhanced agentic capabilities". It's 304 billion parameters - 167GB on Hugging Face - but it appears to punch well above its weight.

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Anecdote

Using DeepSeek-V4-Flash-0731's default reasoning level via OpenRouter produced a disappointing pelican image, while raising the reasoning effort to high yielded a much better result.

Recounts a hands-on test: the default reasoning level via OpenRouter gave a disappointing pelican image, but bumping reasoning level up to high produced something much better.

transcript

Author: I got a disappointing pelican from it using the default reasoning level via OpenRouter: But when I bumped reasoning level up to high I got something much better: llm -m openrouter/deepseek/deepseek-v4-flash-0731 -t pelican -o reasoning_effort high

explains mechanism · 1

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