DeepSeek V4.1 Flash Pricing: The Full Rate Card, and the Cache-Hit Number That Decides Your Bill

DeepSeek V4.1 Flash has been generally available since 2026-09-10, and as of today its published rate card is the cheapest thing in the top tier of the model market: $0.15 per million input tokens, $0.60 per million output, and $0.003 per million on cache hits. Those three figures are the off-peak column. During DeepSeek's weekday peak windows every one of them doubles, cache hits included. The comparison that matters is not against DeepSeek's own ageing flagship — DeepSeek-V4-Pro-0813 lists at $0.66 / $1.98 per million off-peak, so Flash undercuts its own sibling by roughly 4x on input — but against the closed frontier tier that buyers actually price against: GPT-5.6 Sol, Claude Opus 5 and Gemini 3.8 Flash, each of which charges an order of magnitude more per token.

One correction before the numbers, because the leak that preceded this launch is still circulating. The figures that spread before GA described a price cut landing "tomorrow." The prices are real; the framing was not. V4.1 Flash shipped on 2026-09-10 with these rates already in force, and DeepSeek's own changelog records the reduction as part of the release rather than as a later cut. Everything below is read off DeepSeek's live pricing page today, 2026-09-16.

The full rate card, as of 2026-09-16

DeepSeek publishes one sheet covering the current SKUs, with every line item split into a peak and an off-peak column. For the model id deepseek-flash, which serves DeepSeek-V4.1-Flash, the published rates are:

• Input, cache miss — $0.15 per 1M tokens off-peak; $0.30 per 1M at peak

• Input, cache hit — $0.003 per 1M tokens off-peak; $0.006 per 1M at peak

• Output — $0.60 per 1M tokens off-peak; $1.20 per 1M at peak

• Context window — 1M tokens, with a 384K maximum output

• Concurrency limit — 2,500 concurrent requests on the Flash SKU

• Legacy model ids — deepseek-v4-flash and deepseek-v4-flash-vision-exp are retired as separate products but still route to V4.1 Flash, billed at Flash prices

The gap between the first two lines is the whole story of this sheet. A cache hit costs 50x less than a cache miss off-peak — a 98% discount, which is also how Artificial Analysis renders it in its cost model. Nothing else on the card moves a bill that far.

Peak is not a rounding error, and it is timed for Beijing

DeepSeek's peak windows are Monday to Friday, 01:00–04:00 UTC and 06:00–10:00 UTC. Everything outside them — including all weekend hours — bills at half rate. The doubling applies to all three lines, so a peak-hour cache miss costs $0.30 and peak output costs $1.20.

Where those windows land depends entirely on where you are. In Beijing they are 09:00–12:00 and 14:00–18:00 — two working blocks, which is the point. In Berlin, in September, the second window is 08:00–12:00 CEST: a European team's entire morning is peak. In New York the same window is 02:00–06:00 EDT. A US-based team running normal hours is effectively never billed at peak, and an EU team pays double every morning before lunch. If you are choosing when to run a batch job, that is a decision worth $0.15 per million tokens and it costs nothing to make.

What cache-hit pricing actually does to an agent bill

Cache hits are automatic on DeepSeek — the docs say disk caching is enabled by default for all users with no code change — so the discount is not something you have to architect for. It is also best-effort, not guaranteed: DeepSeek states there is no fixed TTL, an unused cache is cleared "usually within a few hours to a few days," and the system does not promise a 100% hit rate. Worth reading the response's prompt_cache_hit_tokens and prompt_cache_miss_tokens fields before you model anything on this.

The arithmetic matters more than the adjective. Take a coding agent with a 40,000-token stable prefix — system prompt, tool schemas, repo context — run for a 60-turn session, where each turn appends about 2,000 tokens of tool output and the model emits about 1,200 tokens. Total input across the session is 5.94M tokens and total output 72,000 tokens.

Billed with no caching at all, off-peak: 5.94M input at $0.15 is $0.891, plus 72,000 output at $0.60 is $0.043 — about $0.93 for the session.

Billed with the prefix cached, which is what actually happens by default: 5.782M of those input tokens arrive as cache hits at $0.003 ($0.017), 158,000 arrive as cache misses at $0.15 ($0.024), and the same 72,000 output tokens cost $0.043. About $0.084 — roughly 11x cheaper. Input falls from 95% of the bill to 49% of it, the cached portion alone is 21%, and the output tokens become the largest single line. Same model, same session, same output — the only thing that changed is whether the prefix was reused.

Note what is not on DeepSeek's sheet: a cache-write line item. Anthropic charges 1.25x base input to populate a 5-minute cache and 2x for an hour; DeepSeek's card lists only hit and miss, and its caching guide describes no write or storage fee. If you are comparing caching economics across vendors rather than headline token rates, that absence is worth more than the 50x hit discount suggests.

It is also why the pass-through detail on DeepSeek V4.1 Flash on OrcaRouter matters here. We bill at the provider's own list rate with zero markup, so a vendor price change is live on our side the same day rather than waiting on a repricing pass — and the cache-hit and peak/off-peak columns you see above are the ones you are actually billed against, not a blended approximation of them.

Versus DeepSeek-V4-Pro-0813: a 4x gap, and a retirement that did not happen

The obvious internal comparison is the flagship SKU, and it is less dramatic than the headline rate suggests. DeepSeek-V4-Pro-0813, the model id deepseek-v4-pro, lists at $0.66 per 1M input on a cache miss and $1.98 per 1M output off-peak, doubling at peak to $1.32 and $3.96. Its cache hits cost $0.022 off-peak — more than 7x Flash's.

• Cache-miss input — $0.15 vs $0.66 off-peak, so Flash is 4.4x cheaper

• Output — $0.60 vs $1.98 off-peak, so Flash is 3.3x cheaper

• Cache-hit input — $0.003 vs $0.022 off-peak, so Flash is 7.3x cheaper

• Context and output ceiling — identical at 1M and 384K on both SKUs

• Concurrency — 2,500 on Flash against 500 on V4 Pro, a 5x difference that disappears from the price sheet entirely

Three things about this comparison are easy to get wrong. First, the reuse argument is stronger on the flagship in absolute terms even though Flash carries the bigger multiplier: caching one million tokens saves $0.638 on V4 Pro and $0.147 on Flash, because the flagship's miss rate is so much higher to begin with. Second, the model everyone expected to be gone is not: DeepSeek announced it would stop serving V4 Pro on 2026-09-14 and route those requests to Flash, drew a developer backlash over swapping a backend model under production workflows, and reversed the decision on 2026-09-11. V4 Pro is still being served, at unchanged billing. Third, and this is the trap the leak coverage walked into — there is no released V4.1 Pro. DeepSeek-V4-Pro-0813 remains the flagship SKU, and the vendor has not shipped a successor under that name. Anyone pricing a migration to "V4.1 Pro" is pricing a model that does not exist.

Versus the closed frontier tier

Against the closed models buyers actually shortlist, the multiplier is the headline. All figures below are from each vendor's own published pricing page today.

• GPT-5.6 Sol — lists at $5.00 per 1M input and $30.00 per 1M output on OpenAI's short-context tier, currently carrying a promotional $4.00 / $20.00 rate that OpenAI says is available at least through 2026-11-21, with cached input at $0.40. Against the promotional rate, DeepSeek V4.1 Flash is 26.7x cheaper on input and 33.3x on output; against list, 33x and 50x. Sol's cache hit costs 133x what Flash's does.

• Claude Opus 5 — $5.00 per 1M input and $25.00 per 1M output, with cache hits at $0.50 per 1M on Anthropic's published sheet. That is 33x Flash's input rate, 42x its output rate, and 167x its cache-hit rate. Anthropic's 5-minute cache writes add another 1.25x on top; DeepSeek's sheet has no equivalent line.

• Gemini 3.8 Flash — $0.75 per 1M input and $3.75 per 1M output through 2026-12-31, with both rates scheduled to double on 2027-01-01, cached input at $0.075, and — this is the line that recurs on a real bill — cache storage at $0.50 per 1M tokens per hour. Flash is 5x cheaper on input, 6.25x on output and 25x on cache hits against it, and DeepSeek charges nothing to hold a cache.

The capability context is what keeps this honest. On Artificial Analysis's Intelligence Index v4.3, DeepSeek V4.1 Flash (reasoning, max effort) scores 40 and ranks 6th of 113 models, at $0.27 per index task and 214.4 output tokens per second. That is a genuinely frontier-adjacent placement at a price 26x below the tier it is being compared to — but the same measurement shows it is one of the most verbose models AA has tested, generating 250M output tokens across the index run against a median of 140M. Verbosity does not change the per-token price and it does change the bill. A model that writes 62% more tokens than the median still costs far less here, but "cheap per token" and "cheap per task" are different claims, and only the second one is what you pay.

Is there a free tier?

No. DeepSeek's own pricing page documents no free API tier, no free monthly quota and no free model — billing is pay-as-you-go against a topped-up or granted balance, with granted balance consumed first. The consumer chat app is free; the API behind deepseek-flash is not, and there is no free endpoint for it on DeepSeek's platform. Several third-party gateways advertise free or trial access to this model, and we would treat all of them as prototyping surfaces rather than production backends, because those terms change without notice and none of them carry the vendor's own rate card.

The efficiency claims behind the price

The price is not a subsidy, at least on DeepSeek's own account. The vendor's model card and technical report describe V4.1 Flash as a 552B-parameter mixture-of-experts on a Causal Encoder-Decoder layout that activates roughly 8B parameters per token during prefill and 16B during decode — asymmetric by design, cheap to read with and more expensive to write with. On the memory side DeepSeek reports a global KV cache of about 890 bytes per token, roughly a quarter of DeepSeek-V4-Flash's, and a persistent cache footprint around one eighth of it under identical workloads, achieved by discarding sliding-window state and replaying the last 128 tokens on a hit. These are vendor-reported measurements on the vendor's own hardware, and the technical report does not claim mathematical equivalence to full computation for the replay path.

The parameter count is the one figure in this release that is genuinely unsettled, and it is worth being precise about why. DeepSeek's prose reports 552B, and that is the backbone — the part that does the computing. Sitting alongside it is Engram, a conditional-memory lookup table the model card sizes at 196B parameters, accessed by token lookup rather than computed through. Add them and you land near 748B, which is the number a widely-read community analysis on r/LocalLLaMA argued for within hours of the weights landing, and which the Hugging Face repository's own file panel pushes higher still, toward 763B. Community deployment write-ups have counted the checkpoint at about 510 GB across 48 shards, shipped natively quantised as FP8 dense weights with FP4 experts. Treat the total as disputed and the backbone figure as vendor-reported. The active-parameter counts are the ones that drive speed; the resident weights are the ones that decide whether the model starts at all.

Self-hosting is a real alternative to this price, under MIT

The weights are at deepseek-ai/DeepSeek-V4.1-Flash under the MIT licence, which permits commercial use, modification and redistribution. That makes the API rate card a choice rather than a toll: at MIT, nothing in the licence prevents you from serving this model yourself and paying for compute instead of tokens.

What it does not make it is cheap to do. The checkpoint is roughly 510 GB of resident weights before cache and activations, DeepSeek states no GPU requirement anywhere in the repository, and community deployment write-ups put the practical floor at a multi-GPU node rather than a workstation. Three serving stacks shipped day-0 support on 2026-09-10 — vLLM, SGLang with Miles, and Cambricon's vLLM-based NeuWare adaptation for its own accelerators — but on release day vLLM and SGLang shipped dedicated preview images rather than official packages, and llama.cpp had not merged V4.1 architecture support. Consumer-hardware self-hosting is not the alternative to the API price today; a rented 8-GPU node is. If your volume is large enough to amortise that, the licence lets you; if it is not, $0.15 per million tokens is the cheaper answer and it is not close.

What the rate card actually asks you to decide

Three things, in order of how much money they move. Keep a stable prompt prefix and let the cache do its work — it is automatic, it is a 50x discount, and on a 60-turn agent loop it turns a $0.93 session into an $0.084 one. Run your batch traffic outside 01:00–04:00 and 06:00–10:00 UTC on weekdays, which for a US team is a no-op and for a European team means moving the morning job to the afternoon. And if you are pricing this against DeepSeek's own flagship, remember the flagship is still on sale — the scheduled retirement was cancelled on 2026-09-11, both SKUs sit behind one key, and the switch between them is a model-id change rather than a migration.

What the rate card does not tell you is whether the model is good enough for your workload, and the numbers for that are newer and thinner than the price sheet. The Artificial Analysis index placement is a single measurement at max reasoning effort, the vendor benchmark rows are vendor-reported, and the parameter count is in dispute. The price is the settled part of this release. Price your migration on it, and test the capability before you commit to it.