$ cat wiki/models/deepseek-v4-flash.md
DeepSeek V4-Flash
Spec
| Attribute | Value |
|---|---|
| Developer | DeepSeek |
| Released | 2026-07-31 (official / public beta, build 0731; preview earlier) |
| Announced | 2026-07-31 |
| Context window | unknown |
| Pricing | $0.14/M input (cache miss) · $0.0028/M input (cache hit) · $0.28/M output — see Conflicting Reports |
| License | MIT (open-weight) |
| Availability | DeepSeek API (Responses format, Codex-adapted), Hugging Face (deepseek-ai/DeepSeek-V4-Flash-0731) |
Release Date
2026-07-31, moving V4-Flash out of Preview into public beta under the build
designation 0731 (source).
The release is post-training only — same 284B total / 13B active MoE architecture as the Preview, re-post-trained for agentic tasks, no architectural change (source).
A statement accompanying the release says "The official release of DeepSeek-V4-Pro will follow soon" (source).
Benchmarks
Vendor-stated figures as relayed by coverage; the primary model card was not readable from this environment. The comparison column is DeepSeek's own larger V4-Pro-Preview (source):
| Benchmark | Flash Preview | Flash 0731 | V4-Pro-Preview |
|---|---|---|---|
| Terminal Bench 2.1 | 61.8 | 82.7 | 72.1 |
| DeepSWE | 7.3 | 54.4 | unknown |
| Cybergym | 38.7 | 76.7 | unknown |
| NL2Repo | 39.4 | 54.2 | unknown |
| Toolathlon-Verified | 49.7 | 70.3 | unknown |
| Coverage reports the retrained Flash beating V4-Pro (Preview) on **nine agent | |||
| benchmarks** despite a far smaller activated parameter count | |||
| (source). |
Per Eval Harness Configuration, these are vendor-run numbers on agentic benchmarks with no published harness configuration, so they are recorded as claims about a (model, harness) pair rather than as model properties.
Architecture
- MoE, 284B total / 13B active — unchanged from the Preview
- Self-hosting: minimum a single H100 for FP8, or roughly 170 GB VRAM for full weights (source)
Use Cases
- Agentic coding and terminal/tool use — the benchmarks that moved are all agentic
- Cost-sensitive serving where V4-Pro is too expensive
- Local and self-hosted inference under MIT
Compared To
| Model | Terminal Bench 2.1 | Open? |
|---|---|---|
| DeepSeek V4-Flash 0731 | 82.7 (vendor-stated) | Yes (MIT) |
| DeepSeek V4-Pro-Preview | 72.1 (vendor-stated) | Yes (MIT) |
| DeepSeek V4 (V4-Pro-Max) | unknown | Yes (MIT) |
Conflicting Reports
- Pricing. The rates above ($0.14 input cache-miss / $0.28 output) come from digitalapplied's release write-up. AlphaSignal's headline for the same model quotes "$0.08 per million tokens". Nothing captured reconciles the two — $0.08 may be a blended or off-peak rate, but no source read says so (source).
- Artificial Analysis placement. r/LocalLLaMA reports the model at 50 on the Artificial Analysis Intelligence Index, 1 point below GLM-5.2 and GPT-5.6. This is a community report of a third-party index, not a DeepSeek claim; this repo's own Artificial Analysis snapshots do not yet carry the model, so it is recorded but not quoted as a benchmark (source).