deepseek-v4.1-flashLyceum
- api
- chat
- hosting
- EU
- model lab
- DeepSeek
- weights
- open
- added
- April 2026
- model id
- lyceum/deepseek-v4.1-flash
capabilities 5/8
11 providers serve deepseek-v4.1-flash. This page is one of them. Compare all endpoints
- input
- $0.50
- output
- $1.50
- cache write
- -
- cache read
- $0.13
worked cost
| volume | cost |
|---|---|
| 100K in + 10K out | $0.0650 |
| 1M in + 100K out | $0.65 |
| 10M in + 1M out | $6.50 |
Requesty charges what the upstream provider charges, with no markup and no per-request fee. Prompt caching and smart routing cut the effective cost further on repeated context. Gateway pricing
- median ttft
- 1.60s
- median tok/s
- 267.0
- blended /1M
- $0.26
- cache hit
- 77.3%
- answered
- 81.00%
Weekly aggregates from production traffic routed through Requesty to this endpoint. A metric with no qualifying sample is left out rather than filled in. Compare against the other providers
no published scores for this exact variant. region deployments and highspeed tiers usually share the base model's results
- retention
- none
- trains on prompts
- no
- hosted in
- EU
Terms are the provider's, not Requesty's: routing a request here puts it under them. Lyceum Privacy Policy
Need every request to stay in one jurisdiction? EU hosting routes only through EU-hosted providers.
lyceum/deepseek-v4.1-flashthis id pins the lyceum deployment, with no routing and no failover. the managed id on the canonical page routes across all 11 providers instead
from 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)
Change the base url to https://router.requesty.ai/v1, use your Requesty key, set the model to the id above. Existing OpenAI SDK code needs no other edit, and the same key reaches every other model in the catalog. Browse all models
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.
questions 5
How much does deepseek-v4.1-flash cost?
What is the context window of deepseek-v4.1-flash?
What can deepseek-v4.1-flash do?
How do I use deepseek-v4.1-flash with the OpenAI SDK?
Can I run deepseek-v4.1-flash through Requesty?
more from lyceum 5
| endpoint | ctx | in /M |
|---|---|---|
| glm-5.3-flash | 1.0M | $0.20 |
| glm-5.3 | 1.0M | $1.75 |
| deepseek-v4-flash-0731 | 1.0M | $0.25 |
| kimi-k3 | 1.0M | $3.00 |
| deepseek-v4-pro | 1.0M | $1.75 |
call deepseek-v4.1-flash through one endpoint
One key for this endpoint and 600+ other models. No markup on provider prices, automatic failover when a provider degrades, and prompt caching built in. Methodology