The Claim
DeepSeek-V4-Flash-0731 is now the official V4 Flash release, superseding the preview. The model card states that on all nine agentic benchmarks it lists, Flash-0731 beats V4-Pro (Preview) — "despite its far smaller activated parameter count." That is a strong claim, so I checked it two ways: the table arithmetic, and the architecture in config.json.
9/9, Verified Row by Row
| Benchmark | Flash-0731 | V4-Pro (Preview) | GLM-5.2 | Opus-4.8 |
|---|---|---|---|---|
| Terminal Bench 2.1 | 82.7 | 72.1 | 81.0 | 85.0 |
| NL2Repo | 54.2 | 38.5 | 48.9 | 69.7 |
| Cybergym | 76.7 | 52.7 | – | 83.1 |
| DeepSWE | 54.4 | 12.8 | 46.2 | 58.0 |
| Toolathlon-Verified | 70.3 | 55.9 | 59.9 | 76.2 |
| Agents' Last Exam | 25.2 | 16.5 | 23.8 | 25.7 |
| AutomationBench Public | 25.1 | 12.8 | 12.9 | 27.2 |
| DSBench-FullStack † | 68.7 | 41.8 | 61.8 | 71.6 |
| DSBench-Hard † | 59.6 | 31.1 | 54.5 | 71.7 |
Flash-0731 wins 9/9 against V4-Pro (Preview), wins all 8 rows where GLM-5.2 has a score, and loses all 9 to Opus-4.8 — the closest margins being Agents' Last Exam (25.2 vs 25.7) and AutomationBench (25.1 vs 27.2). "Broadly competitive" is precise: close, not ahead. Caveats from the card itself: all numbers are DeepSeek's own evaluations (DeepSeek Harness minimal mode, max reasoning effort), and the two DSBench sets are marked † as internal DeepSeek test sets. None of this is independently reproduced.
What config.json Says
From the public config: model_type deepseek_v4, 43 layers, hidden_size 4096, 64 attention heads with a single KV head, vocab 129,280, and max_position_embeddings 1,048,576 — 2^20, so the "million-token context" in the paper title is literal. The MoE is 256 routed experts plus one shared expert per layer, with 6 routed experts active per token and an expert intermediate size of just 2048. Dense weights are FP8 (E4M3, dynamic activation); expert_dtype is fp4 — experts ship at 4 bits by design.
Reconciling 284B and 304B
One expert = up (4096×2048) + gate (4096×2048) + down (2048×4096) = 25,165,824 parameters. With 43 × 256 = 11,008 routed experts, that is ≈ 277.0B in routed experts alone. Adding shared experts, dense attention, and the (unshared) embeddings and head lands at ≈ 284B — matching unsloth's 284B figure. The card's "304B in safetensors" includes the in-checkpoint DSpark speculative-decoding module (11.3 GB at BF16) and quantization scale tensors. Different accounting, not a contradiction.
Activated per token: 7 experts per layer (6 routed + 1 shared) × 43 layers ≈ 7.6B, plus dense attention and embeddings ≈ 10B — roughly 3.5% of the model. The "far smaller activated parameter count" statement is consistent with the architecture.
The Engineering News: Speculation Is In the Checkpoint
DSpark ships inside the main checkpoint: 7 speculative tokens, greedy draft sampling, no separate draft model to deploy. vLLM: --speculative-config '{"method":"dspark","num_speculative_tokens":7}'; SGLang: --speculative-algorithm DSPARK; llama.cpp users can add it as a module (Q8_0, 10.9 GB). The release also adds three-level reasoning_effort (low/high/max, up to 384K output tokens at high/max) and replaces the Jinja chat template with an encoding/ folder of Python helpers — teams using transformers directly will want to migrate.
Local Deployment, Sized Honestly
unsloth's GGUF: UD-Q4_K_XL 155 GB, UD-Q8_K_XL 162 GB — the "lossless" Q8 is only 7 GB bigger than Q4 per unsloth's note. Ollama: ollama run hf.co/unsloth/DeepSeek-V4-Flash-0731-GGUF:UD-Q4_K_XL. llama.cpp: llama serve -hf unsloth/DeepSeek-V4-Flash-0731-GGUF:UD-Q4_K_XL. A 155 GB Q4 file means big-RAM workstations or multi-GPU servers; for most product teams, the hosted flash tier will be the economical path.
Takeaways
- The flash tier's economics are now backed by the vendor's own benchmarks — but read them as the vendor's.
- FP8 dense + FP4 experts + in-checkpoint speculation is a deployment recipe worth copying for latency- and cost-sensitive agent workloads.
- A 1M-token context with a 384K output budget changes what fits in a single agent call.
Model card: https://huggingface.co/deepseek-ai/DeepSeek-V4-Flash-0731 (MIT) · config.json: https://huggingface.co/deepseek-ai/DeepSeek-V4-Flash-0731/resolve/main/config.json · GGUF: https://huggingface.co/unsloth/DeepSeek-V4-Flash-0731-GGUF · Paper: https://arxiv.org/abs/2606.19348
Not tested / not run — no inference or benchmark reproduction was performed. Benchmark numbers are the vendor's; the arithmetic and config reading are mine.
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