DeepSeek V4.1 Flash: what changed, and how to use it over the API
On September 10, 2026 DeepSeek released DeepSeek V4.1 Flash — not a retrain this time, a new generation: 552B parameters (mixture-of-experts, 8B active per token in prefill and 16B in decode), a new “causal encoder–decoder” architecture, vision trained in from the start of pre-training, a 1M-token context, and MIT weights on Hugging Face at deepseek-ai/DeepSeek-V4.1-Flash (the tech report PDF ships in the same repo).
It replaced the previous build in our Core Pool the same day. There is exactly one thing to migrate: the model id. The new id is deepseek-v4.1-flash; the old deepseek-v4-flash keeps working as an alias until October 10, 2026, after which it returns an invalid-model error. Same key, same endpoints, same subscription, same reserved hours — unlimited, flat-rate access starts from $17.99/mo ($15.29/mo billed annually), live pricing on /pools.
# before # from 2026-09-10"model": "deepseek-v4-flash" "model": "deepseek-v4.1-flash"What DeepSeek measured
Section titled “What DeepSeek measured”The headline claim in DeepSeek’s own release material is that V4.1 Flash beats its own larger V4-Pro on performance, cost, speed and total time to finish a task. It is confident enough in that to act on it: from September 14, 2026 DeepSeek will route its own deepseek-v4-pro traffic to V4.1 Flash, billed at Flash rates, until a V4.1-Pro exists.
Here are the three agent benchmarks from the instruct table on the model card, next to V4-Pro and Claude Opus 5.0. All of these are vendor-run — DeepSeek’s own harness at maximum reasoning effort (reasoning_effort=100), with no independent verification yet:
Same numbers as a table, plus the base-model scores DeepSeek publishes alongside them:
| Instruct (vendor harness, max reasoning effort) | V4.1-Flash | V4-Pro | Claude Opus 5.0 |
|---|---|---|---|
| Terminal-Bench 2.1 | 90.6 | 87.9 | 89.1 |
| DeepSWE v1.1 (resolved) | 74.2 | 62.7 | 74.0 |
| AutomationBench (pass@1) | 54.8 | 43.2 | 50.3 |
| Base model | V4.1-Flash-Base | V4-Flash-Base | V4-Pro-Base |
|---|---|---|---|
| MMLU-Pro | 74.1 | 68.3 | 73.5 |
| HumanEval | 79.4 | 69.5 | 76.8 |
| GSM8K | 93.0 | 90.8 | 92.6 |
Two honest readings, and both are worth holding at once. The generous one: on DeepSeek’s own harness, a model with 8–16B active parameters edges past Claude Opus 5.0 on all three agent benchmarks, and past its own Pro-tier sibling on every row of both tables — which is exactly why DeepSeek is willing to point Pro traffic at it. The sceptical one: every number above was produced by the vendor, at maximum reasoning effort, on its own scaffolding. Vendor tables set expectations; they don’t settle them. Where independent evaluation lands is the next section.
Where it lands on the independent board — pending
Section titled “Where it lands on the independent board — pending”Artificial Analysis has not published an Intelligence Index score for V4.1 Flash yet. So we are not moving anything on the strength of a vendor table: our Pareto Frontier, Price Tracker and Which-LLM reports keep the previous build’s plotted point until an independent score exists. For reference, the build this one replaces scored 50 on that index when it launched (52 after AA’s later v4.1.1 recalibration). We’ll update the reports when AA publishes.
The architecture, the vision, and the price
Section titled “The architecture, the vision, and the price”| DeepSeek V4.1 Flash | |
|---|---|
| Parameters | 552B mixture-of-experts — 8B active per token in prefill, 16B in decode |
| Architecture | New causal encoder–decoder: a 20-layer causal encoder followed by a 20-layer decoder |
| Vision | Native — a from-scratch vision encoder plus projector, trained alongside text from the start of pre-training, not bolted on afterwards |
| Context window | 1M tokens |
| KV cache | ~890 bytes per token — DeepSeek reports roughly ¼ of the previous generation’s HBM footprint and ⅛ of its SSD footprint |
| Weights | MIT — deepseek-ai/DeepSeek-V4.1-Flash, tech report in the repo |
| Model id here | deepseek-v4.1-flash (old deepseek-v4-flash accepted until 2026-10-10) |
| Pool | Core Pool, alongside MiMo v2.5 |
The cache line is the one with second-order consequences. A quarter of the KV footprint per token is what makes a 1M-token context economically serious rather than a spec-sheet number — and it is the mechanism behind DeepSeek’s list price coming down on a newer, larger model, which is not the usual direction:
| DeepSeek list price, per 1M tokens | V4 Flash (before) | V4.1 Flash (now) |
|---|---|---|
| Off-peak input | $0.22 | $0.15 |
| Off-peak output | $0.66 | $0.60 |
| Peak input | $0.44 | $0.30 |
| Peak output | $1.32 | $1.20 |
| Cache hit (off-peak / peak) | — | $0.003 / $0.006 |
Peak hours are 01:00–04:00 and 06:00–10:00 UTC, Monday to Friday; everything else is off-peak. DeepSeek has retired the deepseek-v4-flash id on its own API, where it now routes to V4.1.
On a flat-rate subscription none of that column matters at all — a Core Pool block costs the same whether your agent burns one million tokens or one billion — but it matters as a signal: the cheap tier keeps getting better without getting more expensive. We track that trend month by month in the LLM Price Tracker.
How to use it
Section titled “How to use it”curl:
curl https://api.cheapestinference.com/v1/chat/completions \ -H "Authorization: Bearer YOUR_API_KEY" \ -H "Content-Type: application/json" \ -d '{"model": "deepseek-v4.1-flash", "messages": [{"role": "user", "content": "Hello"}]}'OpenAI SDK (Python):
from openai import OpenAI
client = OpenAI( base_url="https://api.cheapestinference.com/v1", api_key="sk-...", # your subscriber key)
response = client.chat.completions.create( model="deepseek-v4.1-flash", messages=[{"role": "user", "content": "Fix the failing test in this repo..."}],)print(response.choices[0].message.content)Anthropic SDK (Python) — the same key against the /anthropic endpoint:
from anthropic import Anthropic
client = Anthropic( base_url="https://api.cheapestinference.com/anthropic", api_key="sk-...", # your subscriber key)
message = client.messages.create( model="deepseek-v4.1-flash", max_tokens=1024, messages=[{"role": "user", "content": "Explain this stack trace..."}],)print(message.content[0].text)No account yet? Register, subscribe to the Core Pool, and mint a key at cheapestinference.com/keys.
Run it in Claude Code
Section titled “Run it in Claude Code”Claude Code speaks the Anthropic Messages API, so it drops in with two environment variables (plus the small-model one):
export ANTHROPIC_BASE_URL="https://api.cheapestinference.com/anthropic"export ANTHROPIC_AUTH_TOKEN="sk-..." # your subscriber keyexport ANTHROPIC_MODEL="deepseek-v4.1-flash"export ANTHROPIC_SMALL_FAST_MODEL="deepseek-v4.1-flash"Start claude as usual — every request runs on V4.1 Flash with no per-token meter. Per-project pinning, thinking blocks and the rest of the setup are in the Claude Code + DeepSeek guide. The same key works in Cline, Roo Code, Continue and anything that accepts a custom OpenAI base URL.
Sending an image
Section titled “Sending an image”Vision is native, and it uses the standard content formats on both endpoints — image_url parts on /v1/chat/completions, image blocks on /anthropic/v1/messages, in user messages, inside the same 1 MB per-request budget as everything else:
response = client.chat.completions.create( model="deepseek-v4.1-flash", messages=[{ "role": "user", "content": [ {"type": "text", "text": "What does this dashboard screenshot show?"}, {"type": "image_url", "image_url": {"url": f"data:image/png;base64,{b64}"}}, ], }],)Common questions
Section titled “Common questions”Does DeepSeek V4.1 Flash have open weights?
Yes — MIT, on Hugging Face at deepseek-ai/DeepSeek-V4.1-Flash, with the tech report PDF in the same repo. MIT means unrestricted commercial use, including self-hosting.
Does it replace V4-Pro?
For DeepSeek’s own traffic, effectively yes for now: from September 14, 2026 requests to deepseek-v4-pro will be routed to V4.1 Flash and billed at Flash rates, until a V4.1-Pro ships. On its own tables V4.1 Flash outscores V4-Pro on every row we quote above.
What is its Artificial Analysis Intelligence Index score? There isn’t one yet — AA has not published a score for V4.1 Flash. Our living reports keep the previous build’s point on the chart until it does; we’ll update them when it lands.
Do I have to change anything to keep working?
One line: the model id becomes deepseek-v4.1-flash. deepseek-v4-flash still resolves here until October 10, 2026, then returns an invalid-model error. Keys, endpoints, subscriptions and reserved blocks are untouched.
Can it read images?
Yes, natively — vision was in the pre-training, not added afterwards. Send image_url parts (OpenAI format) or image blocks (Anthropic format) in user messages, within the 1 MB per-request limit.
Is it better than Claude Opus? On DeepSeek’s own three agent benchmarks above it is ahead of Claude Opus 5.0 — 90.6 vs 89.1, 74.2 vs 74.0, 54.8 vs 50.3. Those are vendor-run numbers at maximum reasoning effort with no independent verification, and two of the three margins are inside a point; treat them as a claim worth testing on your own workload, not a settled ranking.
Full details: DeepSeek V4.1 Flash API docs · Plans & Limits · what a flat monthly DeepSeek subscription changes.
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