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DeepSeek V4 Flash scores 50 on the Artificial Analysis Intelligence Index, matching Gemini 3.6 Flash and up 10 points from the preview launch in April

DeepSeek V4 Flash 0731 is one Intelligence Index point behind GPT-5.6 Luna (max, 51).  Even after OpenAI's 80% price cut on GPT-5.6 Luna today …

Artificial Analysis

Context & Ripple Effects

DeepSeek’s V4 cycle moved from an April preview that the company said trailed the frontier by several months to an official V4 Flash API public beta positioned around stronger agent capabilities. The current independent score supplies a comparable measure of that progress rather than relying solely on vendor claims.

The result also arrives alongside OpenAI’s reported 80% price cut for GPT-5.6 Luna, making capability comparisons increasingly inseparable from the cost of deploying a model.

First-order effects

  • DeepSeek can market V4 Flash as level with Gemini 3.6 Flash on this index and only one point behind GPT-5.6 Luna, narrowing the perceived quality gap for buyers evaluating fast models.
  • OpenAI’s Luna price cut immediately changes the value comparison for customers weighing DeepSeek’s newly public V4 Flash API beta against a near-leading alternative.

Second-order effects

  • Model buyers and API-platform partners will have greater reason to run task-specific evaluations across DeepSeek, Gemini, and OpenAI rather than treating benchmark standing as a proxy for a single default provider.
  • Rivals face pressure to pair incremental benchmark gains with clearer pricing or deployment advantages, since a one-point index lead may be less decisive after a major price reduction.

Third-order effects

  • If similar capability convergence persists, AI procurement is likely to shift toward cost per useful task, reliability, and integration quality rather than headline benchmark separation.
  • The durable advantage may move from owning the highest-scoring general model to controlling distribution and the surrounding API, agent, and enterprise workflow stack; the available coverage does not establish which provider will win that layer.

The trend: Frontier-model competition is becoming a value race in which rapidly closing benchmark gaps and aggressive API pricing jointly reshape enterprise selection.

Discussion

  • @andrewcurran_ Andrew Curran on x
    DeepSeek V4 Flash ending up in this neighborhood is really impressive. When V4 Pro, the large version, arrives it will probably shake things up like last time. [image]
  • @artificialanlys @artificialanlys on x
    DeepSeek V4 Flash 0731 scores 50 on the Artificial Analysis Intelligence Index …
  • @artificialanlys @artificialanlys on x
    DeepSeek V4 Flash 0731 scores 1559 Elo on GDPval-AA v2, our evaluation for agentic real-world work tasks, up from 1189 for the previous DeepSeek V4 Flash [image]
  • @artificialanlys @artificialanlys on x
    DeepSeek V4 Flash 0731 scores -16 on the AA-Omniscience Index, a 7 point improvement over DeepSeek V4 Flash (-23), driven entirely by a lower hallucination rate. The hallucination rate falls 11 points to 84% while accuracy is unchanged at 37%, consistent with the model being unch…
  • Yadullah Duman Yadullah Duman on linkedin
    The official DeepSeek v4 Flash release is very interesting!  It outperforms the preview version of v4 Pro, is similar and maybe even better …
  • Aman Pandey Aman Pandey on linkedin
    DeepSeek V4 Flash 0731 looks genuinely practical for agent workloads, but the reason is easy to misread.  It is a 284B MoE with only 13B parameters active per token, not a 13B model. …