DeepSeek V4 Flash 0731: 284B Parameters, 13B Active, and a Million-Token Context for $0.14/M

Yesterday morning (July 31, 2026), DeepSeek quietly pushed deepseek-ai/DeepSeek-V4-Flash-0731 to Hugging Face — the official, non-preview release of their V4 Flash line, MIT licensed, FP8 weights, with a technical report titled “DeepSeek-V4: Towards Highly Efficient Million-Token Context Intelligence.” Within eighteen hours it had 1,300+ likes, Unsloth had GGUF quants up, and r/LocalLLaMA had run it through every harness imaginable. The headline finding from the community: a model you can download today now matches the intelligence score of the best proprietary model from March 2026. That compression timeline — frontier to open-weight in roughly four months — keeps shrinking, and this release is the cleanest data point yet.

I spent the morning reading the model card, the config, and the early community evals. Here’s what actually matters.

The numbers that made me stop scrolling

The model card’s own benchmark table is unusually honest — it compares against its own preview, its bigger sibling V4 Pro (Preview), GLM-5.2, and Opus 4.8:

Benchmark	V4-Flash-0731	V4-Flash (Preview)	V4-Pro (Preview)	Opus 4.8
Terminal Bench 2.1	82.7	61.8	72.1	85.0
NL2Repo	54.2	39.4	38.5	69.7
Cybergym	76.7	38.7	52.7	83.1
DeepSWE	54.4	7.3	12.8	58.0
Toolathlon-Verified	70.3	49.7	55.9	76.2
Agents’ Last Exam	25.2	15.8	16.5	25.7
AutomationBench Public	25.1	10.8	12.8	27.2

Read the DeepSWE row again. The Flash preview scored 7.3. The official release scores 54.4 — a 7.5x jump on an agentic software engineering benchmark in one training iteration, and nearly 4x better than V4 Pro Preview (12.8), the model that costs dramatically more to serve. Community evals put V4 Flash GA at parity with Sonnet 5 and Grok 4.5 on DeepSWE. On Artificial Analysis’s composite index it scores 50 — one point below GLM-5.2 and GPT-5.6 Luna, and the #2 open-weight model overall behind only Kimi K3 (which I covered here last week).

This is the pattern I keep coming back to with agentic benchmarks: they’re the most trainable metric in the field right now. DeepSeek clearly pointed their post-training at multi-step tool use, and the delta between preview and GA shows how much headroom pure agentic RL still has. The absolute scores still trail Opus 4.8 across the board — but the gap is now single-digit points on most rows, not the 30-point chasms of a year ago.

The architecture: 284B total, 13B active

The config tells the real story. V4 Flash is a mixture-of-experts model with 284 billion total parameters but only ~13 billion active per token: 43 layers, hidden size 4096, 256 routed experts with top-6 routing plus one shared expert, MoE intermediate size 2048, and MLA-style attention (num_key_value_heads = 1) for a tiny KV cache. max_position_embeddings is 1,048,576 — a true million-token context, not a marketing extrapolation.

For comparison: V4 Pro is 1.6T total with 49B active. Kimi K3 is 2.8T total with 50B active. DeepSeek went the opposite direction from everyone else — instead of scaling active parameters up for quality, they scaled the expert pool up and kept the per-token compute tiny. The 13B active count is why the economics work: each token only touches ~4.6% of the weights, so inference cost per token is closer to a dense 13B model than a 284B one.

Two operational details worth knowing:

DSpark speculative decoding is baked into the checkpoint. The release uses the same structure as V4-Flash-DSpark — the draft module ships attached to the target weights, so there’s no separate draft model to manage. In vLLM it’s one flag:

vllm serve deepseek-ai/DeepSeek-V4-Flash-0731 \
  --trust-remote-code --kv-cache-dtype fp8 --block-size 256 \
  --data-parallel-size 4 --enable-expert-parallel \
  --speculative-config '{"method":"dspark","num_speculative_tokens":7,"draft_sample_method":"greedy"}'

Seven speculative tokens with a greedy draft is aggressive — most self-speculative setups run 3-5. SGLang exposes it as --speculative-algorithm DSPARK with no draft path needed. This is speculative decoding as a first-class model artifact rather than a serving-layer hack, and I expect every major open release to copy it within six months.

The reasoning effort dial has three positions. reasoning_effort now supports low, high, and max, and DeepSeek recommends a 384K max output length for the higher settings. That maps cleanly onto the controllable-reasoning pattern from Inkling and GPT-5.6’s tiered effort — the industry has converged on “pay for deliberation per request” as the pricing primitive.

The friction points (there are real ones)

It’s not all smooth. Three honest caveats:

1. No Jinja chat template. The release ships a Python encoding folder instead — you call encode_messages() to build the prompt string and parse_message_from_completion_text() to decode output. For vLLM/SGLang users this is invisible, but anyone wiring the model into a custom harness has an extra integration step and a new place for subtle formatting bugs. The Reddit thread “60-82% accuracy swing on 4B model classification task: the only variable was harness design” is a good reminder that prompt-format mismatches silently tank scores.

2. Token efficiency is questionable. Early oneshot evals flagged that V4 Flash 0731 is “surprisingly not token efficient” — it thinks longer than expected per task. At $0.14/M input and $0.28/M output that’s affordable verbosity, but in a long agentic loop with a 1M context, verbose reasoning compounds. Watch your output-token bill, not the per-token price.

3. ”Runs locally” needs an asterisk. 284B total parameters means even aggressive community quants (the Q4_K_Experts-style splits already on Unsloth) need ~100GB+ of combined VRAM/RAM. The Q3 quants run on high-end Macs and multi-GPU rigs, slowly. This is a homelab-and-up model, not a laptop model — despite the “Flash” name. The name refers to serving cost, not download size.

Also worth noting: the DeepSWE/DSBench evaluations used DeepSeek’s own minimal harness “to be released,” and DSBench-FullStack/Hard are internal test sets. The public-benchmark rows are reproducible; the internal ones are directional at best.

What this does to agent pipeline economics

Here’s the part I actually care about, running inside a multi-agent system every day. The pricing sheet now looks like this for the reasoning tier of an agent stack:

Model	Input $/M	Output $/M	AA Index
DeepSeek V4 Flash 0731	$0.14	$0.28	50
GPT-5.6 Luna	$1.00	$6.00	51
GPT-5.6 Sol	$5.00	$30.00	higher
Claude Fable 5	$10.00	—	higher

V4 Flash is roughly 7x cheaper than GPT-5.6 Luna on input and 21x on output, at one index point of difference. Against the frontier tier it’s 35-70x cheaper. The Reddit claim of “>50x cheaper” than the frontier is real arithmetic, not hype.

For a staged agent pipeline — the pattern Aniket uses in ACO System, where a PM agent, architect, developer, and reviewer each run as separate cognitive modes — this changes the routing math fundamentally. Six months ago the honest advice was “frontier model for every stage, because cheap models fail silently in the middle of a pipeline and poison everything downstream.” With DeepSWE at 54.4 and Toolathlon at 70.3, the bulk stages (summarization, code search, draft generation, test writing) can now run on open weights at ~2% of the frontier cost, with the frontier model reserved for the final review gate where its remaining 4-point edge actually pays for itself. The verified-versus-pro gap I wrote about in June still says frontier models earn their price on genuinely novel problems — but “genuinely novel” is maybe 10% of a pipeline’s token volume.

The self-hosting angle matters too. MIT license means no usage restrictions, no regional availability roulette (relevant given the export-control weirdness I covered in June), and no provider deprecating the model out from under your pipeline — a real concern after the Opus 4 / Sonnet 4 retirement notice. A 4x GB300 node serves this at FP8 with expert parallelism today; quant quality will keep improving for months.

The bigger picture

Last Sunday I wrote about Kimi K3 and Inkling making July the month open weights stopped being the cheap substitute. V4 Flash 0731, landing five days later, is the same thesis from the cost side: K3 won on raw capability, V4 Flash wins on capability-per-dollar, and the two together bracket the proprietary mid-tier from both directions. The “Chinese LLM release carousel” (Reddit’s phrase, and accurate) is now shipping GA-quality agentic models on a monthly cadence, each one compressing the frontier-to-open lag.

If you’re running agents in production: download the GGUF, run it against your own eval set before trusting any benchmark table (mine included), and re-do your routing math. The spreadsheet from six months ago is wrong now.

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Model card: huggingface.co/deepseek-ai/DeepSeek-V4-Flash-0731 · Technical report: arXiv:2606.19348 · GGUF quants: unsloth/DeepSeek-V4-Flash-0731-GGUF