DeepSeek V4.1-Flash Beats V4 Pro at a Seventh of the Cost, So DeepSeek Retired the Pro

DeepSeek V4.1-Flash Beats V4 Pro at a Seventh of the Cost, So DeepSeek Retired the Pro

7 min readSeptember 12, 2026

Quick verdict

DeepSeek launched V4.1-Flash and then quietly started retiring its own flagship. The new model is open weights under an MIT license, has a 1M-token context, takes text and image input, and uses a new causal encoder-decoder architecture that splits the model into a small prefill path and a slightly larger decode path. Artificial Analysis scored it 40 on its Intelligence Index, ahead of the older V4 Pro, and priced a full benchmark run at about $0.27 per task, roughly seven times cheaper than GLM-5.3 or Kimi K3. The catch is verbosity: this is one of the chattiest models anyone has measured. The cost still comes out low because the per-token price is so low, not because the model is careful with words. For anyone paying for AI by the token, that trade is the whole story.

What actually shipped

V4.1-Flash went up on Hugging Face with weights, a tech report, and same-day API access. The headline spec from Artificial Analysis is a causal encoder-decoder design with 8B active parameters on input and 16B active on output, a 1M-token context, text and image input, an MIT license, and first-party API availability. The total size caused a fight. DeepSeek's materials and early write-ups threw around 552B, but a Hugging Face teardown on r/LocalLLaMA added up the safetensors and put the real figure near 763B once you count the 551B backbone, a 197B "engram" lookup component, the optional MTP module, and the vision encoder. So the active compute is tiny, but the thing you have to store is not.

The architecture is what got researchers talking. Sebastian Raschka called it a "big overhaul" and said DeepSeek should have called it V5, pointing at the encoder-decoder split as the real break from prior generations. The reason for all of it is the KV cache. nrehiew read the report as a case study in obsessing over KV cache compression, and cited a striking ~890 bytes per token at the benchmarked quality. DeepSeek's own claim is a 4x reduction in HBM and 8x on SSD versus the previous generation, and 437x versus its first-generation model. That is the lever behind serving a 1M context this cheaply.

The price is the story

Most launches lead with a benchmark. This one leads with a receipt. Artificial Analysis put V4.1-Flash at $0.30 per million input tokens and $1.20 per million output, with cached input at $0.006 per million and an extra 50% off-peak discount on top. Even though the model is verbose, a full Intelligence Index run costs about $0.27, against $2.01 for GLM-5.3 and $2.00 for Kimi K3, and around $0.67 for the outgoing V4 Pro. Vals reached the same conclusion from a different harness and named it the number one open-weight model on its board at $0.30 per test.

The verbosity is real and worth planning around. Artificial Analysis measured V4.1-Flash at about 89k tokens per Intelligence Index task, 25% more than GLM-5.3 and above even Fable 5.1 and Claude Opus 5. The task cost stays low only because each token is so cheap; the model itself is a heavy user of them. On a long agent run where you pay per token and the model burns them freely, do the math on your own workload before assuming the sticker price carries over.

Where the benchmarks landed

The scores back up the price. On AutomationBench-AA, V4.1-Flash hit 69%, tying GPT-6 Astra and edging out Grok 4.6 at 67%, while jumping 15 points over the older V4 Flash and 12 points over V4 Pro. On the agentic GDPval-AA v2 board it gained 164 Elo, from 1468 to 1632, passing Kimi K3 at 1584. On the long-context AA-LCR test it scored 84%, level with GPT-5.6 Sol and Gemini 3.8 Flash. Those are frontier-adjacent numbers from an open model that costs a fraction of the closed field.

Not everyone bought the headline. teortaxesTex, usually a DeepSeek watcher, called some results "very strange", singling out the AutomationBench top spot and a regression elsewhere, and argued DeepSeek tends to post strong internal evals that get brittle in real workflows. Worth holding in mind: the model is cheap and scores well, but the gap between a leaderboard and a production agent is where DeepSeek has stumbled before.

DeepSeek soft-retired V4 Pro

The clearest signal of DeepSeek's own confidence is that it stopped serving its flagship. According to a widely shared thread on r/LocalLLaMA, V4 Pro traffic now routes automatically to V4.1-Flash at the cheaper Flash pricing until a V4.1 Pro arrives. On the API, the old deepseek-v4-flash, deepseek-v4-flash-vision-exp, and eventually deepseek-v4-pro names point at the new model, and callers are told to switch to deepseek-flash. Commenters read V4 Pro's quiet exit as an admission that the bigger, pricier model was not pulling its weight, with one noting it was "not performing meaningfully better than the flash model despite being nearly 6 times the size." When a lab replaces its top model with a cheaper one and bills you less for the switch, that tells you which way the cost curve is bending.

Video: DeepSeek V4.1-Flash tested

A hands-on run through the new model, including the 200-plus tokens-per-second local numbers and how it stacks up against Astra.

Can you run it yourself

Sort of, and that is the interesting part. Because active compute is tiny, people got it running through heavy offload. Fraser Price reported 300-plus tokens per second on four RTX Pros at full precision with under 32GB of peak system RAM, streaming the bulk of the model off an NVMe drive, and antirez showed it running on a 128GB M5 Max with SSD streaming. But the 763B stored size means most people are not fitting this on a workstation, even a well-equipped one. For the vast majority, "open weights" means renting it from a provider. Baseten and Ollama added support at launch, with Ollama rolling it out to Max and Team accounts first, then Pro subscribers.

What it means if you pay for AI

The pattern from the last few open-weight launches repeats. A model that is close to the frontier on coding and agent work, priced at a fraction of the closed field, does not need to win outright to change your bill. It just needs to be good enough that routing the bulk of your work to it becomes the obvious call, while you keep Claude Opus or GPT-6 for the genuinely hard reasoning. That model-by-task split is exactly the case for running everything through one AI aggregator instead of paying for a stack of separate subscriptions, and for coding agents in particular, where token burn is constant, a model this cheap is hard to leave out of the rotation.

FAQ

Is DeepSeek V4.1-Flash actually cheaper than V4 Pro?

Yes, by a wide margin. A full Intelligence Index run costs about $0.27 on V4.1-Flash versus roughly $0.67 on V4 Pro, and DeepSeek is now routing V4 Pro API traffic to the Flash model at Flash pricing. See our write-up of the V4 Pro launch for the model it just replaced.

Can I run V4.1-Flash on my own machine?

Only if you have serious hardware and patience for SSD offload. Active compute is tiny, but the stored model is around 763B parameters, so for most people "open" means renting it from a provider like Baseten or Ollama rather than self-hosting. Our guide to the best AI coding agents covers where cheap open models fit in a real workflow.

Why does the low price come with a warning?

V4.1-Flash is one of the most verbose models measured, at about 89k tokens per benchmark task. The total cost stays low because the per-token price is so cheap, but on long agent runs that token count adds up, so check it against your own usage before switching everything over.

Sources

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