deepseek-v4.1-flash
Fireworks AI/🇺🇸 US/chat
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 Fireworks at $0.22 input, $0.007 cache read and $0.66 output per 1M tokens. Open weights are published on Hugging Face. Ideal for coding agents, long context RAG, high throughput batch processing and multimodal assistants.MoreLess
Which id to call
fireworks/deepseek-v4.1-flashThis exact deployment on Fireworks AI, with no routing and no failover. Send it as the model field.
Input /1M
$0.22
$0.0070 cached
Output /1M
$0.66
3.0x input
Context
1.0M
393K output
Added
Sep 2026
chat
Capabilities 5/8
Provider rates
What deepseek-v4.1-flash costs
Provider prices per 1M tokens, updated September 11, 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
$0.22
Output /1M
$0.66
Cache write /1M
-
Cache read /1M
$0.0070
What a workload costs
- 100K input + 10K output
- $0.0286
- 1M input + 100K output
- $0.29
- 10M input + 1M output
- $2.86
At the rates above, before caching. A cache read costs $0.0070 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.
fireworks/deepseek-v4.1-flashThis exact deployment on Fireworks AI, 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="fireworks/deepseek-v4.1-flash", messages=[ {"role": "user", "content": "Explain quantum computing in one paragraph."}, ], ) print(response.choices[0].message.content)
Reference
Specs and data terms
What the catalog reports for this deployment, and what the provider does with the traffic.
Released 2026-09-10
Benchmark scores
Benchmarks measure the model, not this endpoint, so they are the same wherever these weights are served.
Artificial Analysis Intelligence Index: a composite of multiple evaluations measuring overall model capability.
Scores from official model cards, Artificial Analysis and public leaderboards. They measure specific skills and do not capture every aspect of model quality, so test on your own workload.
Same provider
More from Fireworks AI
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 $0.22 per million input tokens and $0.66 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 393K tokens per response. That's roughly 1,398 words of input you can fit in a single prompt.
How does deepseek-v4.1-flash perform on benchmarks?
deepseek-v4.1-flash scores 51.9% on SciCode, 39.5% on Intelligence Index, 39.2% on Humanity's Last Exam. See the full benchmark chart above for results across MMLU Pro, GPQA Diamond, SWE-Bench Verified, HumanEval, MATH, AIME, MMMU, and LiveBench.
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 "fireworks/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 Fireworks AI. You do not host the model yourself: point base_url at Requesty, set the model to "fireworks/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.