AI model
DeepSeek's V4 Flash, a low-cost language model in the DeepSeek V4 family, offered via API and third-party hosts with a 1M-token context window.
The quarter-size global KV cache and eighth-size persistent storage are serving-cost claims rather than benchmark scores, and banking the second one requires a prefix-cache tier your stack has to already run.
Perspective Coverage
6 publishersReality
OpenAI has cut input prices on its Luna models from $1.00 to $0.10 per million tokens since July 30, over two rounds of reductions. Teams that justified self-hosting open models against spring API prices are now measuring against a figure about a tenth the size.
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Harness v0.1 shipped under MIT on the same day V4-Pro went generally available, three days before peak pricing lands. The lock-in it targets is the runtime, not the weights.
Perspective Coverage
4 publishersReality
Every off-peak rate sits above the old flat price, and Pro cache hits jumped roughly 6x. Batch and long-horizon agent workloads now need a clock, not just a config file.
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LiveReview's Maneshwar says Gemini File Search cost over 50 cents a review run because a reasoning model performed each search. A local index and a cheaper model brought runs down to 4 cents.
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V4-Flash-Vision-Exp is live on DeepSeek's API and costs a fraction of Anthropic's price. The vendor's own table shows it trailing on eight of eleven tests, including a 12-point gap on repository work.
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Enterprises buy the cheapest model that clears their bar. On Ramp's July billing data, that leaves Anthropic's flagship with about an eighth of its maker's platform spend.
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V4.1-Flash retires the V4 Pro line and carries two active-parameter counts, 763B total with 8B on input tokens and 16B on output, so one sizing number no longer covers both phases of a request. Baseten had it running on day zero.
Perspective Coverage
3 publishersReality
Flash's off-peak input price is under a quarter of what V4-Pro cost, and on DeepSeek's own table it beats the old Pro checkpoint on Terminal-Bench, but it scores 36.8 on Humanity's Last Exam and no V4.1-Pro has a date.
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GLM-5.3-Flash leads agentic terminal work, DeepSeek V4 Flash is billed as the cheapest per token, and a 2.52B MiniCPM5-2B runs locally under Apache 2.0. The comparison flags most of those numbers as vendor-reported.
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Sentient Labs put a coach model in charge of improving a worker model on a spreadsheet benchmark, and the resulting score jump sat on top of a grading harness that leaked answers in one direction and mismarked correct work in the other.
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One endpoint fronts more than 300 models, but the company running the GPUs picks the inference engine and the quantization. A dev.to writeup says the quality gap that follows turns up in the response body, while the status code still reads success.
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The V4.1-Flash change log puts Terminal Bench 2.1 at 90.6 against V4-Pro's 87.9 and DeepSWE at 74.2 against 62.7. DeepSeek says API prices came down with the release and points to a pricing page for the amounts.
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Shrivu Shankar rented cloud GPUs, pointed about 100 abliterated open-source agents at his own name, and five hours later two weak passwords and three old side projects had fallen while his Gmail, 1Password and bank held.
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The model card for this 552B-parameter Mixture-of-Experts release puts the global KV cache at 890 bytes per token, about a quarter of the previous Flash generation, and every figure in it is DeepSeek's own.
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The reroute drops the peak output rate from $3.96 to $1.20 per million tokens and puts a different model behind every V4-Pro call, on a date DeepSeek picked. The migration work lands on whoever parses the output.
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Perplexity pairs about 190 million web pages with 69,721 agent-written queries, which is closer to production than most retrieval tests get, and keeps the corpus, queries and labels private so it stays the only party able to run it.
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The efficiency claims are self-reported and measured against DeepSeek's own prior model, but the weights are MIT-licensed and already pulled 1.78 million times, which is what turns a ratio into a number a buyer can carry into a renewal.
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Qwen3.8-Flash-Next puts 36 Gated DeltaNet layers and 12 sparse-attention layers on Hugging Face, which means the retrieval budget Qwen4 will inherit is something you can measure against your own traces now.
Perspective Coverage
5 publishersReality
The old flat rate became a peak rate that blends 3.6x higher, with a half-price window covering seventeen hours, so inference cost modelling now depends on which UTC hour the traffic lands in.
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Build · August 31, 2026 · Confirmed2 publishers