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Model details

deepseek-v4-flash-0731

DeepSeek-V4-Flash-0731 is a dated point release in DeepSeek's Flash family, a line positioned around fast, economical inference rather than maximum capability. The variant name itself carries the 0731 snapshot marker, and the identifier was in circulation publicly on its release day: a third-party write-up on the Data Science in Your Pocket publication announced it on July 31, 2026, and within days a user on the NVIDIA Developer Forums was asking the NIM catalogue team to add the -0731 build to their hosted model list. That combination of same-day commentary and rapid downstream hosting interest suggests the snapshot was noticed quickly by the developer tooling ecosystem, which typically matters most for teams that pin a specific version and need predictable behavior across deployments.

Practically, this model fits workflows where a stable, identifiable Flash point release is more valuable than chasing a moving flagship target. Because the -0731 suffix is a snapshot rather than a rolling alias, teams can pin it for reproducibility in agentic pipelines that rely on tool calling, and the open-weight posture means the weights can be inspected, self-hosted, or fine-tuned rather than locked behind a single hosted endpoint. The Flash designation also suits high-volume text work where cost per token and latency dominate, with reasoning behavior and adjustable sampling giving some control over the trade-off between spend and output depth. Version pinning this way is a sensible hedge: it lets you adopt a mid-year checkpoint now and keep a stable baseline while later snapshots arrive.

DevPass (LLM Gateway)deepseek-v4-flash-0731deepseek-flash

Quick Info

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Provider
DevPass (LLM Gateway)
Model key
deepseek-v4-flash-0731
Release date
Jul 31, 2026
Last updated
Jul 31, 2026
Knowledge cutoff
2025-05
Input modalities
Output modalities
Capabilities

Cost

Input token cost
$0.08
Output token cost
$0.18

Limits

Output tokens
384,000 tokens
Context window
1,050,000 tokens

Transparent token rates

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Rates are shown per one million tokens. Combined means one million input plus one million output tokens.

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Latest news about deepseek-v4-flash-0731

DevPass (LLM Gateway)

CoverageBenchmark

Vals AI lists DeepSeek V4 Flash 0731 as an open-weight model from DeepSeek with a July 31, 2026 release date, a 1-million-token context window, and up to 384,000 output tokens. It supports text input but not image, video, or file inputs, giving developers a clear picture of its intended modality and context limits. The independent listing reports an average accuracy of 55.07%, a best Vibe Code Bench v1.1 score of 44 out of 126, and token prices of $0.30 per million input tokens and $1.20 per million output tokens. Other listed results include 89.90% on GPQA Diamond and 72.08% on CyberBench v1.1.

DevPass (LLM Gateway)

Coverage

Unsloth published GGUF distributions of the exact DeepSeek-V4-Flash-0731 checkpoint under the MIT License, making the model available for local quantized deployment. Its Q8 UD-Q8_K_XL version provides full-precision lossless operation according to Unsloth and requires 162GB of storage, only 7GB more than its Q4 UD-Q4_K_XL build. The Unsloth package adds practical tooling for the model: DeepSeek-V4-Flash-0731 can run in Unsloth Studio with High and Max thinking toggles, and the project claims its DSpark support can deliver up to twice the decoding speed. Unsloth remains the distributor, while the model and official technical report are attributed to DeepSeek.

DevPass (LLM Gateway)

Coverage

DeepSeek officially released DeepSeek-V4-Flash-0731, superseding the preview with substantially enhanced agentic capabilities. The model retains the DeepSeek-V4-Flash-DSpark structure with an attached speculative decoding module, and the official card says it outperforms DeepSeek-V4-Pro Preview on the listed benchmarks despite a smaller activated parameter count. Reported results include 82.7 on Terminal Bench 2.1, 54.2 on NL2Repo, 54.4 on DeepSWE, and 70.3 on Toolathlon-Verified. Developers can enable DSpark speculative decoding in vLLM, while the release adds low, high, and max reasoning-effort levels and provides Python encoding tools instead of a Jinja chat template.

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