DeepSeek V4.1 Flash 1017 Deep Dive: Benchmarks & Pricing

DeepSeek V4.1 Flash 1017 Update: Deep Dive Into the 890-Byte KV Cache, the 8B/16B Split, and 77% on DeepSWE

Thirty-nine days after DeepSeek V4.1 Flash first appeared as a temporary beta endpoint, DeepSeek has replaced it with a new checkpoint. The update did not come as a new model name or a new architecture. It came as a date suffix, "1017," pushed to the web app, the mobile app and the API on October 17, 2026, with MIT-licensed weights posted on the same day. DeepSeek calls it a minor update, according to @ItsmeAjayKV. The vendor-reported numbers suggest something bigger than that on agentic coding.

TLDR DeepSeek V4.1 Flash is a 552B open-weight Mixture-of-Experts model. It uses a new Causal Encoder-Decoder design that activates 8B parameters to read input and 16B to generate output. Its compressed KV cache makes off-peak cached input cost $0.003 per 1M tokens. The October 17 "1017" checkpoint reportedly raises DeepSWE v1.1 from 74.2 to 77.0, above Claude Opus 5's 74.0, but it still trails Opus 5 on Terminal-Bench 4.0. Independent evaluations so far cover the September checkpoint only, and they rate the model below the frontier in general intelligence.

Key Takeaways

• Architecture: a 552B MoE backbone plus a 196B Engram Conditional Memory. Asymmetric Activation uses 8B parameters during prefill and 16B during decode.
• Cache economics: DeepSeek claims the KV cache needs 1/4 the HBM and 1/8 the SSD storage of the previous generation. Community posts cite 890 bytes per token.
• 1017 gains (vendor-reported): DeepSWE v1.1 rises to 77.0, and Terminal-Bench 4.0 rises from 31.2 to 42.3. Opus 5 still scores 51.8 on Terminal-Bench 4.0.
• Pricing: off-peak rates are $0.003 for cached input, $0.15 for uncached input and $0.60 for output, per 1M tokens. Peak rates are double.
• Independent view: Artificial Analysis scores the September checkpoint 40 on its Intelligence Index, ranked #6 of 113 in its class. It also flags the model as very verbose.

What Actually Shipped: Three Releases in Six Weeks

The timeline matters because each stage changed what developers could actually call.

September 8, beta. A temporary model ID, deepseek-v4.1-flash-expires-on-0910, appeared on the DeepSeek API. @ItsmeAjayKV reported a 1M context window, more than 333 tokens per second of decode, and low, high and max thinking modes.

Source: @ItsmeAjayKV

September 10, general availability. DeepSeek's official announcement called V4.1 Flash "the smallest model in our new architecture family." It told developers to set the model to deepseek-flash. It retired V4 Flash and V4 Flash Vision Exp and routed their old IDs to V4.1 Flash. The weights went up on Hugging Face alongside a technical report. Thomas Wolf told readers not to be fooled by the modest name, writing that V4.1 Flash "seems like... a major update".

October 17, the 1017 checkpoint. According to @0xLogicrw's summary of the official notes, web, app and API all switched to the new checkpoint at once, and the weights were open-sourced under MIT.

There was a gap between the last two releases. On October 14, @ItsmeAjayKV claimed that DeepSeek had silently upgraded V4.1 Flash on the backend without an announcement. Whether that was an early version of 1017 has not been confirmed.

Inside the Causal Encoder-Decoder

The Causal Encoder-Decoder (CED) is DeepSeek's design for splitting a single 40-layer Transformer into a 20-layer causal encoder and a 20-layer decoder. The decoder projects its global KV cache from the encoder's final hidden states. It does not derive a cache from each decoder layer, according to the model card.

The practical result is Asymmetric Activation. Reading a prompt touches 8B active parameters. Generating a token touches 16B. Agent workloads mostly read: repositories, tool schemas, long histories. The architecture is optimized for that input-heavy pattern.

Three other named components show up in the checkpoint and on derived model cards:

• Engram Conditional Memory. NVIDIA's NVFP4 model card lists "552B backbone in total... plus 196B Engram conditional memory." @Tech2Wild's file breakdown describes Engram as n-gram embedding lookups across two tables of 384M rows each, taking about 203 GB.
• Compressed Sparse Attention 2 (CSA2). NVIDIA's card names it as the attention mechanism. DeepSeek has not published a standalone description in the sources reviewed here.
• DSpark Speculative Decoding. The draft experts ship inside the checkpoint. A community EXL3 build documents dspark_block_size=5, 128 draft experts and top-3 routing.

According to @Tech2Wild, the roughly 510 GB checkpoint breaks down into 296 GB for 384 routed experts per layer stored in 4-bit, 203 GB for Engram, and about 11 GB for attention, embeddings, the vision encoder and MTP heads.

Source: @Tech2Wild

The KV cache is the headline. DeepSeek's launch page claims 1/4 the HBM and 1/8 the SSD storage of the previous generation. ModelScope's launch post puts the native figure at 890 bytes per token, "437x smaller than V1."

There is a caveat for self-hosters. The EXL3 build reports about 3.4 KiB per token in practice under vLLM's fp8_ds_mla layout. Its README notes that "Upstream's '890 B/token' is the model's native FP4 main-KV design, not what this build allocates."

Source: @ModelScope2022

The 1017 Checkpoint: What Changed

DeepSeek's 1017 notes, as summarized by @0xLogicrw, report four changes:

1. DeepSWE v1.1: up from 74.2% to 77.0%, which DeepSeek says beats Claude Opus 5's 74.0%.
2. Terminal-Bench 4.0: up from 31.2% to 42.3%, still below Opus 5's 51.8%.
3. Cost per task: on DeepSeek's official cost curve, the highest-reasoning configuration that reaches 77% averages about ¥1.3 per task. @mylifcc translated that as roughly $0.20.
4. Shorter outputs: DeepSeek says the model now finishes tasks faster and with fewer output tokens.

DeepSeek also highlighted what Chinese-language posts call "许愿式编程," which roughly translates as Wish Programming. Wish Programming is the mode in which the model receives only a short request and plans and builds an entire project by itself. DeepSeek reportedly improved it with Self-Feedback RL, and its demos included a 3D Forbidden City scene, a browser racing game and a Godot-engine remake of Portal 2.

Alongside the checkpoint, DeepSeek opened Search and Fetch APIs backed by its own search engine. Each costs ¥5 per 1,000 successful requests, which @mylifcc gives as $0.75, and call fees are waived for the first two weeks.

The comparison has a gap. As @0xLogicrw pointed out, DeepSeek compared 1017 with Opus 5, not the newer Opus 5.5. That choice sets a lower bar than the current frontier.

Pricing: Why the Cache Rate Is the Real Headline

VentureBeat's launch coverage gives the full rate card. Off-peak, V4.1 Flash costs $0.003 per 1M cached input tokens, $0.15 per 1M uncached input tokens and $0.60 per 1M output tokens. Peak hours run Monday to Friday, 01:00–04:00 and 06:00–10:00 UTC, and peak rates are double.

Model	Cached input ($/1M)	Uncached input ($/1M)	Output ($/1M)
DeepSeek V4.1 Flash (off-peak)	$0.003	$0.15	$0.60
DeepSeek V4.1 Flash (peak)	$0.006	$0.30	$1.20
GPT-5.6 Sol	$0.40	$4	$20
Claude Opus 5	$0.50	$5	$25
Kimi K3	$0.30	$3	$15

Source: VentureBeat, September 10, 2026.

At $0.003 per 1M cached tokens off-peak, DeepSeek V4.1 Flash reads cached context about 167× cheaper than Claude Opus 5's $0.50 cache-hit rate.

VentureBeat's worked example shows why this matters for agents. Take a 500,000-token prefix reused across 100 requests, which adds up to 50M cached input tokens. That costs about $0.15 on V4.1 Flash off-peak, compared with $15 on Kimi K3, $20 on GPT-5.6 Sol and $25 on Opus 5.

The launch page makes the same argument: "Cache-hit charges often account for a large share of agent costs."

Third-party price listings can differ from these numbers. Artificial Analysis lists $0.30 input and $1.20 output, which match DeepSeek's peak rates. Check which band a benchmark's cost-per-task figure assumes before comparing it with anything.

DeepSeek V4.1 Flash vs Claude Opus 5: What the Reports Show

Claude Opus 5 is the competitor that appears most often in DeepSeek's own comparisons and in third-party pricing tables.

• DeepSWE v1.1: 77.0 for V4.1 Flash-1017 against 74.0 for Opus 5. Both numbers are vendor-reported by DeepSeek (@0xLogicrw, @testingcatalog).
• Terminal-Bench 4.0: 42.3 for V4.1 Flash against 51.8 for Opus 5. This is a 9.5-point gap that DeepSeek discloses itself.
• General intelligence (September checkpoint): Artificial Analysis scores V4.1 Flash (max) at 40 on its Intelligence Index. Claude Opus 5 (max) scores 51. On cost per task, it's $0.27 against $5.86, and on speed, 214 against 50 output tokens per second.
• Price: $0.003 against $0.50 for cached input and $0.60 against $25 for output, per 1M tokens, both off-peak (VentureBeat).
• License: V4.1 Flash weights are MIT-licensed. No open weights have been reported for Opus 5.

Against Opus 5.5, Artificial Analysis's release comparison shows a wider capability gap. Opus 5.5 (max) scores 58 against 39 for V4.1 Flash (max). Opus 5.5 also takes 728.27 seconds to first token at max effort, against 1.23 seconds for V4.1 Flash.

On the limited evidence so far, DeepSeek V4.1 Flash appears to match or beat Opus 5 on narrow agentic coding at a small fraction of the cost. Broad reasoning and terminal-heavy work have not been shown to close the gap.

Independent Signals: Where the Picture Gets Messier

Independent results are mixed. All of the following predate 1017.

• Artificial Analysis ranks the model #6 of 113 on intelligence and #4 on speed. It also measured 250M output tokens over the index run, against a 140M median, and calls the model "very verbose."
• Gertlabs, in a Hacker News thread, wrote that because V4.1 Flash is "a heavy reasoner, it sits near Gemini 3.7 Flash on the Pareto front." Gertlabs also found that the model scores about 20% higher in agentic harnesses than in one-shot settings. In the same thread, one user reported per-task costs of "usually 15-30 cents."
• ARC Prize, in a post on X on October 6, reported verified scores of 72.9% on ARC-AGI-2 at $0.13 per task and 94.5% on ARC-AGI-1 at $0.07 per task.
• @mdp_sec scored the model 57/100 on a 100-lab black-box bug-bounty benchmark, behind joint leaders at 63/100.
• Hallucination: a LocalLLaMA thread notes that the model "tops the hallucination benchmark" on AA-Omniscience. Other commenters argue that the test penalizes tool-trained models.
• Safety in agent mode: one r/DeepSeek tester said the model tried to run dangerous commands on OS files outside the repo. The tester's advice: never run it in auto mode.

Local inference is possible but takes serious hardware. @0xSero documented a setup with one RTX 3090 running at 19–26 tokens per second of decode. It still needs 256 GB of RAM and about 200 GB of NVMe storage.

What We Know vs. What We Don't

What we know

• DeepSeek V4.1 Flash is a 552B MoE backbone on a 40-layer Causal Encoder-Decoder, with 8B active parameters during prefill and 16B during decode (model card).
• A 196B Engram Conditional Memory sits on top of the backbone (NVIDIA card).
• The KV cache needs 1/4 the HBM and 1/8 the SSD of the previous generation, and 890 bytes per token are cited natively.
• Off-peak pricing is $0.003 cached, $0.15 uncached and $0.60 output per 1M tokens, and peak is double.
• The weights are MIT-licensed, and reports say the 1017 checkpoint is MIT too.
• 1017 reports 77.0 on DeepSWE v1.1 and 42.3 on Terminal-Bench 4.0. These figures are vendor-reported.

What we don't know

• Whether the 1017 numbers hold up in third-party runs. No independent leaderboard has published 1017-specific results yet.
• V4 Pro's status. The launch page says V4 Pro requests route to V4.1 Flash from September 14. A captured changelog says V4 Pro will continue "in response to user demand."
• Whether 1017's shorter outputs reduce the 250M-token verbosity and the hallucination concerns.
• When V4.1 Pro will ship. DeepSeek has referenced it but given no date.
• Whether the silent backend change reported around October 14 was the 1017 checkpoint.

Why the Model-Swap Policy Matters for Builders

The routing debate deserves more attention than the benchmarks. When DeepSeek first said it would route V4 Pro traffic to V4.1 Flash, commenters on Hacker News objected. One wrote that teams who validated workflows on V4 Pro "might not want to suddenly start testing it in production on V4.1 Flash." An education-sector engineer described how adherence to safety system prompts changed measurably between model versions.

The pattern has now happened again. A reported silent upgrade was followed by a checkpoint swap behind the same deepseek-flash alias. Teams that need behavioral stability should treat the alias as moving.

Here is how to evaluate the model yourself:

• Pin and snapshot. Record outputs on representative prompts before and after October 17.
• Measure cost per finished task. Per-token rates mislead with a heavy reasoner. Count the reasoning tokens.
• Split peak and off-peak. Schedulable batch jobs pay half.
• Compare on a hosted endpoint. If you'd rather not provision 500 GB of weights, you can run the same eval against a hosted DeepSeek-V4.1-Flash and check it against your current production model.

What to Watch Next

Three signals matter over the coming days.

1. An independent 1017 run. Watch for Artificial Analysis or ARC Prize to rescore the new checkpoint. That would show whether the 2.8-point DeepSWE gain and the 11.1-point Terminal-Bench gain hold up outside DeepSeek's own harness.
2. Verbosity data. Run your own coding eval and count output tokens. The claim of shorter outputs is the one most likely to change real costs.
3. Clarity on V4 Pro and V4.1 Pro. Track the DeepSeek API changelog. Routing and retirement notices have changed without much warning twice already.

Try related models on Kie.ai: DeepSeek-V4.1-Flash, Claude Opus 5 and Gemini 3.8 Flash.