deepseek-v4.1-flashTogether AI
- api
- chat
- hosting
- US
- model lab
- DeepSeek
- weights
- open
- added
- September 2026
- model id
- together/deepseek-v4.1-flash
capabilities 5/8
11 providers serve deepseek-v4.1-flash. This page is one of them. Compare all endpoints
- input
- $0.30
- output
- $1.20
- cache write
- -
- cache read
- $0.01
worked cost
| volume | cost |
|---|---|
| 100K in + 10K out | $0.0420 |
| 1M in + 100K out | $0.42 |
| 10M in + 1M out | $4.20 |
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
- 338ms
- median tok/s
- 227.0
- blended /1M
- $0.24
- cache hit
- 37.6%
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
- US
Terms are the provider's, not Requesty's: routing a request here puts it under them. Together AI Privacy Policy
Need every request to stay in one jurisdiction? EU hosting routes only through EU-hosted providers.
together/deepseek-v4.1-flashthis id pins the together ai 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="together/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 552B parameter Mixture of Experts model built on a new Causal Encoder Decoder architecture that activates only 8B parameters for input and 16B for output, giving frontier level intelligence at a fraction of the cost. It is the smallest model in the new DeepSeek architecture family and the first Flash with native vision and multimodal understanding. V4.1 Flash beats DeepSeek V4 Pro on performance, cost, speed and task completion time and scores 88.1 on CyberGym. Its KV cache is compressed to 890 bytes per token, four times smaller than V4 Flash and over 400 times smaller than V1, cutting the cache hit costs that dominate agent workloads. 1M token context window, 384K max output, thinking and non thinking modes, tool calling, structured JSON output and prompt caching. Served on DeepInfra in FP8 at $0.20 input, $0.006 cache read and $0.60 output per 1M tokens, with a flex tier at 0.8x. Open weights are published on Hugging Face. Ideal for coding agents, long context RAG, high throughput batch processing and multimodal assistants.
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 together ai 5
| endpoint | ctx | in /M |
|---|---|---|
| deepseek-v4.1-flash-1017 | 1.0M | $0.30 |
| muse-spark-open | 1.0M | $1.25 |
| glm-5.3-flash | 1.0M | $0.15 |
| glm-5.3 | 1.0M | $1.40 |
| deepseek-v4-pro-0813 | 1.0M | $1.32 |
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