DeepSeek V4.1 Flash Review: The 1M-Context Agentic Model (2026)
By Loïc Jané · Founder, Fleece AI
DeepSeek V4.1 Flash: the model that makes 1M-context agents affordable
At a Glance: DeepSeek-V4.1-Flash is a 552B-parameter open-weights Mixture-of-Experts model that reads images and text across up to one million tokens of context. A Causal Encoder-Decoder design and Compressed Sparse Attention 2 cut the per-token KV cache to 890 bytes — roughly a quarter of DeepSeek-V4-Flash — and the model tops the published agentic tables, with 90.6 on Terminal-Bench 2.1 and 74.2 on DeepSWE v1.1. On Fleece AI it is available from the Starter plan and served on our own infrastructure. Updated September 11, 2026.
DeepSeek released DeepSeek-V4.1-Flash in September 2026 under the MIT license, alongside a technical report that reads more like an infrastructure paper than a model card: the headline claim is not a benchmark number but a hardware cost. The model keeps the same agentic competence as the larger DeepSeek line while compressing the KV cache — the working memory every long conversation consumes — to about 890 bytes per token. That is the number that matters for anyone running multi-step agents, because cache size is what makes long-context inference expensive at serving time.
On Fleece AI, DeepSeek V4.1 Flash is a Starter-tier model: image and text in, 1M-token context, full tool calling, and a reasoning-effort dial from 1 to 100. It runs on a vLLM deployment we operate ourselves, so the per-token cost is our GPU bill, not a provider invoice you would see itemized.
Table of Contents
- Key Takeaways
- What Is DeepSeek V4.1 Flash?
- Architecture: CED, CSA2, and the 890-byte KV cache
- Benchmarks: how it compares to the frontier
- Why KV cache compression matters for agents
- Best Use Cases on Fleece AI
- Getting Started
- DeepSeek V4.1 Flash vs V4 Flash vs Other Models
- Frequently Asked Questions
- The Bottom Line
Key Takeaways
- DeepSeek-V4.1-Flash is a 552B-parameter multimodal MoE model, MIT-licensed, that processes images and text natively and generates text, with a 1M-token context window.
- Its Causal Encoder-Decoder (CED) architecture activates only 8B parameters per token during prefill and 16B during decode — a fraction of the 284B/13B profile of the previous V4-Flash.
- Compressed Sparse Attention 2 (CSA2) plus FP4 KV caching bring the global KV cache to 890 bytes per token, about 1/4 of V4-Flash and 1/437 of DeepSeek-V1.
- On DeepSeek's own agentic tables it posts the best scores on Terminal-Bench 2.1 (90.6), DeepSWE v1.1 (74.2), CyberGym (88.1), AutomationBench (54.8) and Agent's Last Exam (31.8) — ahead of V4-Flash, V4-Pro, K3 and GLM-5.3, and competitive with Opus 5.0 and GPT-5.6 Sol.
- A continuously controllable reasoning effort (1–100) lets you trade thinking time for latency per call, which is exactly the lever an agent platform needs when the same model serves a 200-step flow.
- On Fleece AI the model is available from the Starter plan, with tool calling, vision, and 1M context, on infrastructure we operate.
What Is DeepSeek V4.1 Flash?
DeepSeek-V4.1-Flash is the latest entry in DeepSeek's Flash line — the tier of the family built for high-throughput, long-running agentic work rather than raw benchmark ceiling. Three properties define this generation:
- Multimodal by design. A vision encoder (DeepSeek-ViT, trained from scratch with 2D-RoPE and 3x3 pixel-unshuffle downsampling) and a two-layer MLP projector convert images into embeddings that enter the language model from pre-training, not as a bolt-on. The model reads screenshots, documents, charts and photos alongside text.
- One million tokens of context. Sparse attention was trained at 64K sequence length and extended to 1M over 34T tokens of the 45T-token pre-training corpus. A million tokens is roughly 750,000 words: an entire codebase, a year of support tickets, or a full agent session with its full tool-call history, held in one window.
- Agentic post-training. The SFT → RL → on-policy distillation recipe was kept standard; the effort went into data. DeepSeek synthesized agent tasks and environments at scale, progressively scaling data, tasks, and rollouts — which shows up directly in the agent benchmark tables below.
The weights and the prompt-encoding reference are released on Hugging Face under MIT, so self-hosting is on the table for teams that want it. For teams that would rather not operate GPUs, this is where Fleece AI comes in: the model is one dropdown option among several, already wired to tool calling and 3,000+ app integrations.
Architecture: CED, CSA2, and the 890-byte KV cache
The technical report's central contribution is a reorganized transformer that spends less memory on the same intelligence.
Causal Encoder-Decoder (CED)
The backbone is a 40-layer Transformer split into a 20-layer causal encoder followed by a 20-layer decoder. Instead of building the decoder's KV cache from each decoder layer's own hidden states, the decoder's global KV cache is projected from the final encoder hidden states. The practical effect: prefill — the pass over your input, which dominates agentic workloads where every step re-reads the conversation — activates only 8B parameters per token, and decode only 16B. Input-heavy agent loops are exactly the workload where that asymmetry pays.
Compressed Sparse Attention 2 (CSA2)
Each attention layer is statically assigned one of three modes — Full, Reindex, or Reuse — that share main KV and indexer K across layers and reuse Top-K sparse-attention indices. In the decoder, a Hierarchical Sparse Indexer bounds deeper indexer cost by restricting later indexing layers to a candidate pool built by the first Full-mode layer, independently of context length. FP4 main KV caching (E2M1, one E4M3 scale per 16 channels) does the rest.
SWA Bounded Replay
Sliding-window attention states are no longer persisted to SSD. When a window's KV is gone, the model replays only the most recent tokens to reconstruct it. The persistent KV cache footprint drops to roughly 1/8 of V4-Flash's.
The result
| Component | DeepSeek-V4-Flash | DeepSeek-V4.1-Flash |
|---|---|---|
| Backbone parameters | 284B | 552B |
| Activated parameters (prefill / decode) | 13B | 8B / 16B |
| Global KV cache per token | ~3,560 B | 890 B |
| Context window | 128K | 1M |
| Native vision | No | Yes (DeepSeek-ViT) |
| Routed experts per layer | 256, 8 active | 384, 6 active (+1 shared) |
Two other components are worth knowing. Engram is a 196B-parameter conditional memory sparsely accessed by token lookup — factual recall without paying attention cost on every token. DSpark is speculative decoding with confidence-scheduled verification, which lifts throughput on the decode pass.
Benchmarks: how it compares to the frontier
All instruct-model figures below are from the official technical report at maximum reasoning effort (temperature 1.0, top_p 0.95).
Agentic benchmarks
| Benchmark | Opus 5.0 | GPT-5.6 Sol | K3 | GLM-5.3 | DS-V4-Pro | DS-V4-Flash | DS-V4.1-Flash |
|---|---|---|---|---|---|---|---|
| Terminal-Bench 2.1 | 89.1 | 88.8 | 88.3 | 88.2 | 87.9 | 82.7 | 90.6 |
| Terminal-Bench 4.0 | 51.8 | 39.9 | 12.6 | 37.9 | 12.4 | 7.0 | 31.2 |
| DeepSWE v1.1 | 74.0 | 73.0 | 67.5 | 66.9 | 62.7 | 54.4 | 74.2 |
| CyberGym | — | 84.5 | 80.0 | 84.5 | 83.3 | 76.7 | 88.1 |
| AutomationBench | 50.3 | 45.8 | 46.7 | 48.8 | 43.2 | 37.7 | 54.8 |
| Agent's Last Exam | 28.6 | 26.7 | 27.6 | 28.5 | 25.7 | 25.2 | 31.8 |
| HLE with tools | 63.6 | — | 59.8 | 62.5 | 60.0 | 51.5 | 63.9 |
The pattern is consistent: V4.1-Flash leads its own family by a wide margin and closes to within a point of Opus 5.0 on tool-using agentic work, while costing a fraction of the inference budget at serving time.
Reasoning and code
| Benchmark | DS-V4.1-Flash | Nearest rival (value) |
|---|---|---|
| GPQA Diamond | 90.9 | GPT-5.6 Sol (94.1), Opus 5.0 (93.4) |
| Codeforces rating | 3471 | DS-V4-Pro (3348) |
| MathArena Apex | 65.6 | K3 (65.6), DS-V4-Pro (65.3) |
| HumanEval (base, 0-shot) | 79.4 | DS-V4-Pro (76.8) |
| GSM8K (base, 8-shot) | 93.0 | DS-V4-Pro (92.6) |
Multimodal
As a base model: DocVQA 95.6, CVBench 77.9, MMMU-Pro 56.5, RefCOCO 86.0. With tools, the agentic vision scores are 89.6 on BabyVision and 78.9 on Chartography. For a model whose other job is running terminal agents, the vision side is genuinely production-grade for document and screenshot work.
What the numbers say in practice
- Terminal-Bench 2.1 at 90.6 — the model that writes and executes commands in a Linux container beats the closed frontier on the table. That is the core skill of a Fleece AI agent running shell steps.
- DeepSWE v1.1 at 74.2 — resolved rate on real software-engineering tasks, matched with Opus 5.0 and ahead of GPT-5.6 Sol.
- 1M context across the whole table. DeepSeek ran these agent evaluations with a 1M-token window and 500 steps per agent. Most comparisons at this scale are the point: the model holds a full repo and its entire tool history in context without a summarizer.
Why KV cache compression matters for agents
A chatbot pays the context cost once per user message. An agent pays it on every step: each tool call appends its result, and the next step re-reads everything. At 100 steps, the conversation is the product — and the KV cache is the bill.
At 890 bytes per token, V4.1-Flash holds a 1M-token agent session in roughly 0.9 GB of cache state. V4-Flash needed about 4x that for the same session, and DeepSeek-V1 about 437x. Two consequences:
- Longer sessions fit in the same hardware. Fewer GPU nodes per unit of throughput, which is why self-hosters can run it where V4-Flash did not fit.
- Long-running flows stay cheap. On a fixed subscription like Fleece AI's, the compression means a 200-step flow that re-reads its own history does not quietly become the most expensive thing in your account.
The controllable reasoning effort (an integer from 1 to 100) complements this: a data-extraction step can run low-effort, while a debugging step gets a high-effort pass, all on the same model.
Best Use Cases on Fleece AI
Where V4.1-Flash fits naturally in a Fleece AI account:
- Code and devops agents. Terminal-Bench and DeepSWE leadership maps to agents that read a repo, run commands, and patch files. Pair it with GitHub integration for PR triage and incident response flows.
- Document-heavy back office. DocVQA at 95.6 makes it a strong first choice for invoice, contract, and screenshot processing — send a scan, get structured output.
- Long-history customer operations. A support agent that keeps every prior ticket with a customer in one window, rather than a compressed summary three months stale.
- High-volume batch flows. The 8B/16B activated-parameter profile is built for throughput: nightly data passes where many short, input-heavy steps repeat.
- Multimodal QA. Charts, dashboards, and UI screenshots reviewed as images in the same agent that can act on what it finds.
For the deepest single-shot reasoning — long legal analysis, complex architecture decisions — the Claude Opus and GPT-5.6 Sol tiers remain the heavier hammers. Fleece AI's point is that you choose per agent, not per platform.
Getting Started
Four steps from signup to a running V4.1-Flash agent:
- Create your account — Start the 4-day trial on Fleece AI. The full product is included during the trial, and DeepSeek V4.1 Flash is available from the Starter plan that follows.
- Pick the model — In the chat composer or on any agent, open the model picker and select the new DeepSeek entry. It shows a Starter badge, the agentic tagline, and vision support.
- Connect your first app — Link Slack, Gmail, GitHub or any of the 3,000+ integrations through managed OAuth. Credentials never reach the model; the agent calls tools through a brokered session.
- Describe the workflow in plain language — For example: "Every Monday, pull last week's failed CI runs from GitHub, group them by error type, and post a summary to #eng with the two most common ones." Fleece AI sets up the agent, the schedule, and the run history.
From there you can raise or lower the model's reasoning effort per message, or pin a different model to different agents — the switch is a dropdown, not a migration.
DeepSeek V4.1 Flash vs V4 Flash vs Other Models
| Dimension | DS-V4-Flash | DS-V4.1-Flash | Kimi K3 (Fleece default) |
|---|---|---|---|
| Context window | 128K | 1M | 256K |
| Vision input | No | Yes | No |
| KV cache per token | ~3,560 B | 890 B | n/a (hosted) |
| Terminal-Bench 2.1 | 82.7 | 90.6 | 88.3 |
| DeepSWE v1.1 | 54.4 | 74.2 | 67.5 |
| License | MIT | MIT | Open weights |
Against its predecessor, V4.1-Flash is a strict upgrade on every published axis: bigger context, native vision, a fourth of the cache, and a double-digit lead on the agent tables. Against the closed frontier, the trade is honest — Opus 5.0 and GPT-5.6 Sol still lead pure reasoning (GPQA 93.4/94.1 vs 90.9) — but on the agentic benchmarks that describe what a business agent actually does, V4.1-Flash posts the top or near-top score on the table at open-weights economics.
Within Fleece AI, the split we recommend: V4.1-Flash for anything long-running, tool-heavy, or document-shaped; the GPT-5.6 family and Claude Opus for the occasional high-stakes reasoning step. Both are one selection away from each other per agent.
Frequently Asked Questions
What is DeepSeek V4.1 Flash?
It is a 552B-parameter open-weights Mixture-of-Experts model from DeepSeek, released in September 2026 under the MIT license. It processes images and text natively, supports up to one million tokens of context, and is optimized for agentic workloads through a Causal Encoder-Decoder architecture and compressed sparse attention.
Is DeepSeek V4.1 Flash open source?
Yes. The weights and the model are MIT-licensed on Hugging Face, along with a reference Python implementation of the prompt encoding and a Rust toolkit (deepseek-recipe) for production prompt handling. Self-hosting is feasible if you have the GPU capacity; Fleece AI runs it on our own vLLM deployment so you do not have to.
What is the difference between DeepSeek V4.1 Flash and V4 Flash?
V4.1-Flash roughly quadruples the context window (1M vs 128K), adds native image input, cuts the KV cache to a quarter of the previous size (890 bytes per token), and leads the prior version by a wide margin on agentic benchmarks — 90.6 to 82.7 on Terminal-Bench 2.1 and 74.2 to 54.4 on DeepSWE v1.1.
Can DeepSeek V4.1 Flash read images?
Yes. It is natively multimodal: a dedicated DeepSeek-ViT encoder converts images to embeddings from pre-training. DeepSeek reports 95.6 on DocVQA and 89.6 with tools on BabyVision, which covers documents, screenshots, and chart reading in practice.
How is DeepSeek V4.1 Flash priced?
The model itself is MIT-licensed, so API pricing depends on the provider and self-hosting depends on your hardware. On Fleece AI there is no per-token price: the model is included from the Starter plan (EUR 49 per month, 2,000 credits), and the 4-day trial includes access to the full model catalogue.
How does the reasoning effort setting work?
DeepSeek exposes a continuous dial from 1 to 100: higher values spend more thinking time before answering, improving accuracy at the cost of latency. In Fleece AI you can tune this per agent — a routine data-extraction flow runs low, a debugging agent runs high — without switching models.
Is DeepSeek V4.1 Flash available on Fleece AI?
Yes, from the Starter plan. It supports tool calling across the 3,000+ integrations, image input, and the full 1M-token context window, and it is served on infrastructure Fleece operates rather than a third-party API. It sits alongside Kimi K3 (the app default), the GPT-5.6 family, Mistral Medium 3.5 and the Claude Opus tiers.
The Bottom Line
DeepSeek-V4.1-Flash is the most complete argument yet that the frontier of useful agent work and the frontier of affordable inference are converging. It is a MIT-licensed, multimodal, million-token model that tops its own family's agentic tables, lands within a point of the closed frontier on tool-using benchmarks, and compresses the one cost that grows with every agent step — the KV cache — to 890 bytes per token.
On Fleece AI it is available from the Starter plan as a dropdown choice: image and text in, tools out, 1M context held per session, on hardware we operate. If your agents read more than they write — long histories, documents, repos — it is the model to put on them first.
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Start your 4-day trial on Fleece AI — put DeepSeek V4.1 Flash on your first agent and watch a 1M-context run land in the run history.