deepseek-v4.1-flash
Lyceum/🇪🇺 EU/chat
DeepSeek V4.1 Flash is a sparse mixture-of-experts model from DeepSeek, and the first built on the company's Causal Encoder-Decoder (CED) architecture. It activates 8B parameters on input and 16B on output from a 552B-parameter backbone, an asymmetric split that keeps per-token compute low relative to the model's total size. Image understanding is native to the architecture, with visual and text embeddings trained jointly from the start of pre-training rather than added afterward as in the earlier experimental V4 Flash Vision Exp. It is suited for coding, terminal, and computer-use agents, along with long-horizon tasks that must run to completion across many steps and long-context analysis. Compressed KV caching cuts cache memory to roughly a quarter of the previous Flash generation, significantly reducing costs on agentic workloads. DeepSeek positions it as the cost-efficient tier of the V4.1 family and reports that it exceeds V4 Pro on performance, speed, and task completion time.MoreLess
Which id to call
lyceum/deepseek-v4.1-flashThis exact deployment on Lyceum, with no routing and no failover. Send it as the model field.
Input /1M
$0.50
$0.13 cached
Output /1M
$1.50
3.0x input
Context
1.0M
66K output
Paid /1M
$0.26
measured, cache included
Capabilities 5/8
Provider rates
What deepseek-v4.1-flash costs
Provider prices per 1M tokens, updated November 2, 2026.moreless
These are the upstream provider rates. Pay as you go adds 5%, or 0% if you bring your own keys, and there is no per-request fee. Prompt caching and routing change what you pay against these rates, not the rates themselves.
Input /1M
$0.50
Output /1M
$1.50
Cache write /1M
-
Cache read /1M
$0.13
What a workload costs
- 100K input + 10K output
- $0.0650
- 1M input + 100K output
- $0.65
- 10M input + 1M output
- $6.50
At the rates above, before caching. A cache read costs $0.13 per 1M, so repeated context lands under these figures.
model=
Which id to call
One base url, https://router.requesty.ai/v1, and one key for every id here.
lyceum/deepseek-v4.1-flashThis exact deployment on Lyceum, with no routing and no failover. Send it as the model field.
OpenAI compatible
Call it in three lines
Change the base url, use your Requesty key, set the model to any id on the left. Existing OpenAI SDK code needs no other edit.
123456789101112131415from openai import OpenAI client = OpenAI( api_key="YOUR_REQUESTY_API_KEY", base_url="https://router.requesty.ai/v1", ) response = client.chat.completions.create( model="lyceum/deepseek-v4.1-flash", messages=[ {"role": "user", "content": "Explain quantum computing in one paragraph."}, ], ) print(response.choices[0].message.content)
Live from production
Lyceum on deepseek-v4.1-flash, measured
What this provider was measured doing on deepseek-v4.1-flash across Requesty traffic.moreless
Whole-window figures, because that is the grain published per provider. They cover Lyceum serving this model in every region it serves it from, so a region-pinned deployment shares them with its siblings. A figure is absent where no qualifying sample exists.
First token
1.59s
median
p95 wait
7.74s
slowest 5%
Output speed
267/s
median tokens
Paid /1M
$0.26
blended, cache included
Cache hit
77.2%
of input tokens
Compare these against the other 10 providers serving deepseek-v4.1-flash.
Reference
Specs and data terms
What the catalog reports for this deployment, and what the provider does with the traffic.
Public leaderboards
Benchmark scores
Benchmarks measure the model, not this endpoint, so they are the same wherever these weights are served.
No benchmarks are published for this exact variant yet.
Region-specific deployments and highspeed tiers usually share scores with their base model. Try the base model page or the Lyceum models overview.
Same provider
More from Lyceum
Newest first, on the same provider and the same key.
Reference
deepseek-v4.1-flash questions
How much does deepseek-v4.1-flash cost?
deepseek-v4.1-flash is priced at $0.50 per million input tokens and $1.50 per million output tokens when accessed via Requesty. Prompt caching is supported, which can cut effective input cost by up to 90% on repeated context. Those are the upstream provider rates: pay as you go adds 5% on top, or 0% if you bring your own provider keys.
What is the context window of deepseek-v4.1-flash?
deepseek-v4.1-flash has a context window of 1.0M tokens, with a maximum output of 66K tokens per response. That's roughly 1,398 words of input you can fit in a single prompt.
What can deepseek-v4.1-flash do?
deepseek-v4.1-flash supports vision input, tool calling, extended reasoning, prompt caching, structured outputs (JSON schema). You can call it through any OpenAI-compatible client by pointing base_url to Requesty.
How do I use deepseek-v4.1-flash with the OpenAI SDK?
Install the OpenAI SDK, set base_url to "https://router.requesty.ai/v1", set your API key to your Requesty key, and set the model to "lyceum/deepseek-v4.1-flash". The Quickstart above shows Python, JavaScript and cURL snippets.
Can I run deepseek-v4.1-flash through Requesty?
Yes. deepseek-v4.1-flash runs through Requesty's OpenAI-compatible API, served from Lyceum. You do not host the model yourself: point base_url at Requesty, set the model to "lyceum/deepseek-v4.1-flash", and requests are routed to the upstream provider with automatic failover. The same key gives you 600+ other models too.
Call deepseek-v4.1-flash through one endpoint
One key for this endpoint and 600+ other models. No markup on provider prices, automatic failover, caching built in.