DeepSeek officially released V4.1 Flash on September 10, 2026. The new model swaps out the old V4 Flash and V4 Flash Vision Exp models entirely, brings native image understanding into the base architecture, and cuts Flash API prices by roughly 11 to 57 percent depending on the token type. V4 Pro is also on the way out, with a staged retirement beginning September 14.
Release Status and What Changed
DeepSeek confirmed the launch in its official API changelog, describing V4.1 Flash as the smallest model in a new architecture family. It ships with native multimodal vision, higher throughput, and a design that scales up to larger models in the series. Alongside the weights, DeepSeek also published a full benchmark table and a technical report.
Three practical changes matter most for anyone using the API:
- Use the new model name. The correct name is now deepseek-flash. The old names deepseek-v4-flash and deepseek-v4-flash-vision-exp still work as aliases, but the underlying models are gone. Requests to those names now go to V4.1 Flash at the new rate.
- V4 Pro is getting phased out. Starting 12:00 Beijing time on September 14 (04:00 UTC), all requests to deepseek-v4-pro will route to V4.1 Flash at Flash prices, until V4.1 Pro arrives later.
- New Flash pricing is live, effective 04:00 UTC on September 10. Off-peak rates run 50% cheaper than peak, with peak hours at 01:00 to 04:00 and 06:00 to 10:00 UTC on weekdays. The pricing tables are further down in this article.
DeepSeek ran a two-day beta preview from September 8 under the temporary model ID deepseek-v4.1-flash-expires-on-0910, capped at 20 concurrent requests. That beta window has now closed.
Architecture: Why It Is Faster and Cheaper
This is a ground-up redesign, not a tune-up of V4 Flash. The Hugging Face model card and the technical report lay out the details:
- Scale: 552B backbone parameters plus 196B Engram parameters (a conditional memory module using N-gram hashing), for roughly 748B total. The model activates only 8B per token during prefill and 16B during decode, keeping inference cheap despite the large total count.
- Mixture-of-Experts: 1 shared expert plus 384 routed experts per layer, with 6 routed experts active per token.
- Causal Encoder-Decoder (CED): The 40-layer network splits into a 20-layer causal encoder and a 20-layer decoder. Instead of recomputing KV cache per decoder layer, it projects decoder KV from the encoder’s final hidden states. This roughly halves prefill computation, which matters most when context windows are long and cache misses are frequent.
- Compressed Sparse Attention 2 (CSA2): Each layer runs in one of three modes: Full (compute KV and select attention indices from scratch), Reindex (reuse the shared KV but pick fresh indices), or Reuse (borrow both KV and indices from a prior layer). This cross-layer sharing is the main reason the cache shrinks so dramatically.
- FP4 KV caching: The main KV cache uses MXFP4 quantization, a 4-bit format. Combined with CSA2, this brings the global KV cache down to about 890 bytes per token, roughly one quarter of V4 Flash. That is a 437x reduction compared to DeepSeek V1.
- SWA Bounded Replay: Instead of persisting the full sliding-window attention state to SSD, the model reconstructs it by replaying only the most recent window of tokens. This cuts the persistent cache footprint to about one eighth of V4 Flash with negligible performance loss.
- DSpark speculative decoding: A lightweight 3-block drafter generates 5 draft tokens per forward pass, replacing the MTP module from DeepSeek V3. It is trained separately after pre-training and kept aligned during post-training.
- Native vision: A DeepSeek-ViT encoder trained from scratch handles images at arbitrary resolutions up to about 1344×1344 pixels. Text and image inputs are processed jointly from the start of pre-training, not bolted on afterward.
- Reasoning effort control: Continuously adjustable from 1 to 100 via the API, letting developers trade cost against accuracy. All instruct benchmark figures in this article use maximum effort.
- Training: Pre-trained from scratch on 45T multimodal tokens at 64K sequence length, with 1M token context support. Post-training uses standard SFT plus reinforcement learning, with no algorithmic novelty beyond established practice.
- Open weights: MIT-licensed, with a full technical report covering architecture and evaluation.
Behind the price cuts sits a real engineering change. DeepSeek says V4.1 Flash’s KV cache needs roughly one quarter of the high-bandwidth memory and one eighth of the SSD storage compared to V4 Flash. Compared to DeepSeek V1, the per-token cache is 437 times smaller. The technical report also shows that extending context from 4K to 1M tokens increases decode compute by only about 25%, meaning the model stays fast even at extreme context lengths. For agent workloads that pile up long context windows and hammer cached input tokens, that compression translates directly into lower bills. It is also why DeepSeek can serve more users at these prices without losing money on every request. For a deeper look at why cache hits drive agent costs, see our breakdown of DeepSeek V4 rates and cache economics.
Instruct Benchmarks: V4.1 Flash vs. Frontier Models
DeepSeek published instruct results at maximum reasoning effort with temperature 1.0 and top_p 0.95. Code agent tasks used DeepSeek Harness minimal mode with a 1M context window, visual agent tasks used the Claude Code harness with a 512K window, and remaining agent tasks used official scaffolds. The table below reproduces DeepSeek’s comparison against Opus 5, GPT-5.6 Sol, Kimi K3, GLM-5.3, V4 Pro, and V4 Flash.
| Benchmark | V4.1 Flash | V4 Flash | V4 Pro | GPT-5.6 Sol | Kimi K3 | GLM-5.3 | Opus 5 |
|---|---|---|---|---|---|---|---|
| GPQA Diamond | 90.9 | 89.9 | 92.4 | 94.1 | 92.9 | 88.1 | 93.4 |
| HLE (no tools) | 36.8 (39.1 text-only) | 37.8 text-only | 42.7 text-only | 44.5 | 43.5 | 42.0 text-only | 56.3 |
| Codeforces rating | 3471 | 3289 | 3348 | Not published | Not published | Not published | Not published |
| MathArena Apex | 65.6 | 58.6 | 65.3 | Not published | 65.6 | Not published | Not published |
| Terminal Bench 2.1 | 90.6 | 82.7 | 87.9 | 88.8 | 88.3 | 88.2 | 89.1 |
| Terminal Bench 3.0 | 30.0 | 7.6 | 11.8 | 34.4 | 17.7 | 28.3 | 43.3 |
| Terminal Bench 4.0 | 31.2 | 7.0 | 12.4 | 39.9 | 12.6 | 37.9 | 51.8 |
| DeepSWE v1.1 | 74.2 | 54.4 | 62.7 | 73.0 | 67.5 | 66.9 | 74.0 |
| ProgramBench | 20.3 | Not published | 15.5 | 23.0 | 17.5 | 19.0 | 37.0 |
| NL2Repo-Bench | 64.0 | 54.2 | 61.5 | 56.8 | 58.0 | 58.0 | 75.3 |
| CyberGym | 88.1 | 76.7 | 83.3 | 84.5 | 80.0 | 84.5 | Not published |
| SEC-Bench Pro | 62.8 | 30.9 | 56.4 | 74.3 | Not published | Not published | Not published |
| ExploitGym | 15.3 | 1.8 | 5.4 | 33.7 | Not published | 15.0 | 22.1 |
| HLE with tools | 63.9 | 51.5 | 60.0 | Not published | 59.8 | 62.5 | 63.6 |
| AutomationBench | 54.8 | 37.7 | 43.2 | 45.8 | 46.7 | 48.8 | 50.3 |
| Agents Last Exam | 31.8 | 25.2 | 25.7 | 26.7 | 27.6 | 28.5 | 28.6 |
| Chartography with tools | 78.9 | Not published | Not published | 79.9 | 68.1 | Not published | 84.0 |
| BabyVision with tools | 89.6 | Not published | Not published | 88.9 | 85.7 | Not published | 94.1 |
| ZeroBench-main with tools | 49.0 | Not published | Not published | 53.0 | 41.0 | Not published | 52.0 |
The wins are not subtle. V4.1 Flash beats V4 Flash on every shared benchmark, often by wide margins: DeepSWE jumps from 54.4 to 74.2, Terminal Bench 2.1 climbs from 82.7 to 90.6, and CyberGym moves from 76.7 to 88.1. It also edges out V4 Pro across the board, which gives DeepSeek technical cover for the retirement plan.
Against the frontier competition, V4.1 Flash leads on Terminal Bench 2.1, DeepSWE, CyberGym, HLE with tools, AutomationBench, and Agents Last Exam. It still trails Opus 5 on the newer Terminal Bench 3.0 and 4.0 tests, ProgramBench, and NL2Repo, and GPT-5.6 Sol pulls ahead on SEC-Bench Pro and ExploitGym. These are agent-heavy results, so real-world performance may differ from the numbers.
Base Model Scores and Multimodal Results
Below the instruct layer, the base model tells a more balanced story. DeepSeek compared V4.1 Flash Base against V4 Flash Base and V4 Pro Base under identical internal settings:
- Code and math leads: MMLU-Pro (74.1 vs 73.5 for Pro and 68.3 for Flash), BigCodeBench (60.6 vs 59.2 and 56.8), HumanEval (79.4 vs 76.8 and 69.5), and GSM8K (93.0 vs 92.6 and 90.8).
- Holds steady on Chinese knowledge: C-Eval at 92.1 matches V4 Flash and sits close to V4 Pro’s 93.1.
- Trails V4 Pro on knowledge recall: MultiLoKo (45.5 vs 50.9), SimpleQA Verified (42.3 vs 55.2), SuperGPQA (53.1 vs 53.9), MATH (61.1 vs 64.5), and LongBench-V2 (45.2 vs 51.5).
- New multimodal scores with no V4 equivalent: MMMU-Pro 56.5, CVBench 77.9, DocVQA 95.6, and RefCOCO average 86.0.
The base model picture is more nuanced than the instruct results. V4.1 Flash trades a bit of knowledge recall for stronger code, math, and a native vision pipeline. The gains show up where agent workflows need them most. The native image input continues the direction DeepSeek explored with the experimental V4 Flash Vision model, now built into the base model instead of a separate variant.
How It Compares to Other Close Rivals
Cross-vendor benchmark comparisons always come with a caveat: different testing harnesses, effort settings, and system prompts make one-to-one ranking imprecise. With that in mind, here is how V4.1 Flash stacks up against other close rivals on the benchmarks where figures are available:
- GPQA Diamond: V4.1 Flash at 90.9 sits behind GPT-6 Astra (96.1), Gemini 3.8 Flash (95.3), Grok 4.6 (94.9), GPT-5.6 Sol (94.1), Claude Fable 5.1 (93.7), Muse Spark 1.3 (93.5), Kimi K3 (92.9 to 93.5), and V4 Pro (92.4), but ahead of V4 Flash (89.9) and GLM-5.3 (88.1).
- Terminal Bench 2.1: V4.1 Flash at 90.6 leads the DeepSeek-published set, ahead of Opus 5 (89.1), GPT-5.6 Sol (88.8), and Kimi K3 (88.3). Independent figures for other models may not be directly comparable until a common test harness runs them all.
- DeepSWE: V4.1 Flash scores 74.2, essentially tying Opus 5 (74.0) and pulling ahead of GPT-5.6 Sol (73.0), Kimi K3 (67.5), GLM-5.3 (66.9), and both older DeepSeek models.
- Automation and agent work: V4.1 Flash tops its published set on both AutomationBench (54.8) and Agents Last Exam (31.8), beating Opus 5, GPT-5.6 Sol, Kimi K3, and GLM-5.3 on those same tests.
- Newer-generation tests: On Terminal Bench 3.0 and 4.0, Opus 5 still holds the lead (43.3 and 51.8 vs 30.0 and 31.2 for V4.1 Flash). The model is strong, but not a universal frontier leader.
For context on how Flash-class models have compared with Pro-class models on coding work, see Qwen3.8-Flash matching DeepSeek V4 Pro on coding benchmarks. For pricing context across vendors, see our LLM API pricing comparison.
Flash Pricing From September 10
The new pricing took effect at 4:00 UTC on September 10 (noon in Beijing, midnight Eastern, 9 p.m. Pacific on September 9). Peak rates apply during 1:00 to 4:00 a.m. and 6:00 to 10:00 a.m. UTC on weekdays. Everything else counts as off-peak.
In U.S. dollars, per 1 million tokens:
| Billing item | Off-peak | Peak |
|---|---|---|
| Input, cache hit | $0.003 | $0.006 |
| Input, cache miss | $0.15 | $0.30 |
| Output | $0.60 | $1.20 |
Compared to the previous V4 Flash rates, the biggest win is on cached input. In yuan, that drops from 0.05 to 0.02 (a 60% cut), uncached input falls from 1.5 to 1 (about 33%), and output goes from 4.5 to 4 (roughly 11%). In dollar terms, the off-peak rates of $0.007, $0.22, and $0.66 drop to $0.003, $0.15, and $0.60. For agent workloads that hit the cache heavily, the savings add up fast.
The official DeepSeek API pricing documentation is the authoritative source for the live rates.
Pro Routing and Retirement Timeline
The old V4 Flash and V4 Flash Vision Exp models are gone as of today, though their API names still work as aliases pointing to V4.1 Flash. V4 Pro gets a grace period: it keeps running until 12:00 Beijing time on September 14, after which all traffic routes to V4.1 Flash at the new Flash prices. That bridge stays up until V4.1 Pro ships.
For developers still on V4 Pro, the price difference is dramatic. Pro off-peak rates of $0.022, $0.66, and $1.98 per million tokens drop to Flash’s $0.003, $0.15, and $0.60, cuts of roughly 86%, 77%, and 70%. That said, this is a migration, not just a discount. A retrained architecture can shift prompting behavior, tool calling patterns, and output style even where raw capability goes up. Test before swapping production traffic.
Frequently Asked Questions
What model name should API users call now?
Use deepseek-flash. That is the official name going forward. The old names deepseek-v4-flash and deepseek-v4-flash-vision-exp still work as aliases and route to V4.1 Flash, but there is no reason to keep using them.
When does V4 Pro traffic move to V4.1 Flash?
From 12:00 Beijing time on September 14, 2026 (04:00 UTC), until V4.1 Pro is released. V4 Flash traffic already routes to V4.1 Flash.
What are the new Flash prices?
Off-peak: $0.003 per million cached input tokens, $0.15 per million uncached input tokens, and $0.60 per million output tokens. Peak rates are double, during 1:00 to 4:00 a.m. and 6:00 to 10:00 a.m. UTC on weekdays.
Does V4.1 Flash support images?
Yes. V4.1 Flash handles images natively in the base model, so there is no separate Vision variant to worry about. The API vision guides already use deepseek-flash as the example model name.
Where are the full benchmark tables?
They are in the Hugging Face model card, the technical report, and the September 10 API changelog. Keep in mind that all the instruct scores above used maximum reasoning effort. Lower effort settings will score below these numbers.
Are independent benchmarks available yet?
Not yet. All the numbers above come from DeepSeek’s own tests using DeepSeek harnesses, so treat them as a strong starting point, not the final word. Independent reruns on shared benchmarks will matter before anyone swaps production traffic. Artificial Analysis and Vals.ai have not published V4.1 Flash scores as of publication time.
Conclusion
V4.1 Flash is a meaningful step forward, not just a minor revision. It beats both V4 Flash and V4 Pro across every shared benchmark, holds its own against frontier models on several agent and coding tasks, and costs less to run. DeepSeek claims it can handle over 95% of real-world tasks, and the benchmark spread supports that for coding, automation, and security work. Where it falls short, particularly on the newest-generation Terminal Bench tests, Opus 5 still holds the edge.
The bigger story may be the economics. Between the smaller KV cache, the lower prices, and the retirement of V4 Pro, DeepSeek is making its Flash tier the default choice for most workloads. Once V4.1 Pro arrives, the picture could shift again, but for now, V4.1 Flash looks like the sweet spot for cost-conscious agent development.
