What is DeepSeek V4.1 Flash?
DeepSeek V4.1 Flash is DeepSeek's September 2026 language model for agentic coding, tool-heavy agents, and long-context work. Released on September 10, 2026, it is the first model of DeepSeek's V4.1 family and runs on Unifically as deepseek/deepseek-flash. It has 1M tokens of context, up to 384K output tokens, and thinking on by default. The design is new: a 552B-parameter mixture-of-experts built as a causal encoder-decoder, so only 8B parameters are active while it reads your prompt and 16B while it writes. That split is why long, input-heavy agent sessions stay cheap. On DeepSeek's own agent benchmarks it beats the much larger DeepSeek V4 Pro.
What's new in DeepSeek V4.1 Flash
Coding agents: 74.2% on DeepSWE v1.1
DeepSeek reports 74.2% on DeepSWE v1.1 at max effort, level with Claude Opus 5 at 74.0% and ahead of GPT 5.6 Sol at 73.0% and DeepSeek V4 Pro at 62.7%. Terminal-Bench 2.1 lands at 90.6% against 89.1% for Claude Opus 5. These are DeepSeek's own results, not an independent re-run.
Ahead of V4 Pro on independent testing
Artificial Analysis scores V4.1 Flash at 39 on its Intelligence Index at max effort, above DeepSeek V4 Pro 0813 at 36 and V4 Flash 0731 at 34, and measures it at about 227 output tokens per second against 66 for V4 Pro, as of September 23, 2026. A smaller model now beats the larger one on both quality and speed.
A smaller KV cache for long agent runs
The encoder-decoder design stores about 890 bytes of global KV cache per token, roughly a quarter of what V4 Flash needed, and the persistent cache on disk is about an eighth. Cache hits are a large share of the bill for agents that resend long histories, and that is the cost this change goes after.
Open weights under MIT
DeepSeek published the weights and a technical report on Hugging Face on release day under the MIT license. You can test on the API today and move to self-hosting later without changing models.
Best for
Coding agents
Terminal work, repo edits, and bug fixes run through an agent loop for many steps.
High-volume agent traffic
Background agents where cost per step and output speed matter more than the last few points of quality.
Long-context reading
Large codebases, logs, and document sets read in one 1M token request.
Security and automation tasks
DeepSeek reports 88.1% on CyberGym and 54.8% on AutomationBench, both above its own V4 Pro.
Off-peak batch jobs
Evaluations and bulk processing scheduled outside peak hours to pay the lower rate.
Use cases
Build a coding agent that reads a repository, runs commands, and fixes failing tests across a long session, with cached history keeping each step cheap. Put it behind a support or operations bot that calls tools and needs fast answers at high volume. Use the 1M token window to load a full codebase or a stack of logs and ask for a structured report in one request. Teams already on an OpenAI-compatible stack can switch to it with a model ID change.
Limitations
The lead is on routine agent work. On the harder Terminal-Bench 3.0 and 4.0 suites, DeepSeek's own table puts V4.1 Flash at 30.0% and 31.2%, well behind Claude Opus 5 at 43.3% and 51.8%. Claude Opus 5 also leads on Humanity's Last Exam, 56.3% against 36.8%.
It is verbose. Artificial Analysis counted 250M output tokens to run its index, against a median of 140M, so thinking at high effort adds real output cost. Set reasoning_effort to low for simple calls.
Thinking mode ignores temperature, and when a conversation uses tools, earlier reasoning_content should be sent back with the history.
DeepSeek's model reads images natively, but image input through Unifically is not confirmed yet. Treat it as text in, text out for now.
deepseek-flash is a moving alias. When DeepSeek releases a newer Flash, the same ID answers with the new model. Peak-hour requests bill at 2x the off-peak rate.
DeepSeek V4.1 Flash vs DeepSeek V4 Pro
DeepSeek's own table has V4.1 Flash ahead on the agent suites: 74.2% against 62.7% on DeepSWE v1.1, 90.6% against 87.9% on Terminal-Bench 2.1, and 54.8% against 43.2% on AutomationBench. Artificial Analysis agrees on overall quality, 39 against 36, and measures V4.1 Flash at more than three times the output speed. V4 Pro still wins on knowledge-heavy tests: 92.4% against 90.9% on GPQA Diamond and 42.7% against 36.8% on Humanity's Last Exam. Use V4.1 Flash as the default and V4 Pro when broad world knowledge matters more than speed.
When to use DeepSeek V4.1 Flash
Use DeepSeek V4.1 Flash as the default DeepSeek model for coding agents, tool use, and long-context work where volume and speed matter. Move up to a frontier model like Claude Opus 5 when the task is at the hard end of Terminal-Bench or needs top reasoning on expert questions.
API examples
Call DeepSeek V4.1 Flash by POSTing to /v1/chat/completions, the OpenAI-compatible endpoint shared by every language model on the platform. Parameter docs live at docs.unifically.com/models/llm/deepseek/deepseek-flash.
curl -X POST https://api.unifically.com/v1/chat/completions \
-H "Content-Type: application/json" \
-H "Authorization: Bearer YOUR_API_KEY" \
-d '{
"model": "deepseek/deepseek-flash",
"messages": [
{ "role": "user", "content": "Review this function for race conditions and suggest a fix." }
]
}'
The response comes back synchronously with the answer in content and the thinking in reasoning_content. Set "stream": true to receive tokens as they generate.
FAQs
People also ask
deepseek/deepseek-flash, called through the OpenAI-compatible POST /v1/chat/completions endpoint with your Unifically API key. DeepSeek uses this name as an alias for its latest Flash model, which has been V4.1 Flash since September 10, 2026.
DeepSeek released V4.1 Flash on September 10, 2026. It is the first and smallest model of DeepSeek's new V4.1 architecture family, and the weights are public on Hugging Face under the MIT license.
Yes. DeepSeek models bill at 2x the off-peak rate during peak hours, Monday to Friday from 01:00 to 04:00 and from 06:00 to 10:00 UTC. The rate is fixed when the request starts, so a request that begins off-peak stays at the off-peak rate. Every other hour is off-peak, including all of Saturday and Sunday. Current rates are on the pricing page.
A 1M token context window and up to 384K output tokens in one response. Cached input from repeated prompt prefixes bills at a much lower rate than fresh input, which matters for agents that resend the same history on every step.
In DeepSeek's own testing at max effort it scores 74.2% on DeepSWE v1.1, against 74.0% for Claude Opus 5 and 73.0% for GPT 5.6 Sol, and 90.6% on Terminal-Bench 2.1. On the harder Terminal-Bench 4.0 it drops to 31.2% while Claude Opus 5 holds 51.8%, so the lead is on routine agent tasks, not the hardest ones.
Thinking is on by default and the reasoning text comes back in reasoning_content, separate from the answer. Set reasoning_effort to control how long it thinks: DeepSeek maps the values to three levels, low, high, and max, with medium treated as high.
Much faster. Artificial Analysis measures about 227 output tokens per second for V4.1 Flash against 66 for V4 Pro, and scores it higher on its Intelligence Index, 39 against 36, as of September 23, 2026.
It can. DeepSeek moves deepseek-flash to each new Flash release, so a future Flash version will answer the same ID. Pin your evaluation results to a date and re-run them when DeepSeek announces a new Flash.
