一个要在 9 月 14 日取代 V4 Pro 的模型，有两项分数没打过 V4 ProThe Model Set to Replace V4 Pro on September 14 Loses to It on Two Benchmarks

The official wording, verbatim (consistent across three places: the pricing-page note, the news page, and the changelog):

1. Let's Get This Straight Up Front: It's Not a Comprehensive Sweep

The official wording, verbatim (consistent across three places: the pricing-page note, the news page, and the changelog):

After testing by multiple parties, V4.1 Flash has comprehensively surpassed DeepSeek V4 Pro across every metric, including performance, cost, speed, and total elapsed time, so we plan to retire V4 Pro in an orderly manner. After 12:00 on September 14, 2026, Beijing time, and until a future V4.1 Pro launches, user requests to deepseek-v4-pro will all be routed to V4.1 Flash and billed at V4.1 Flash rates.

That's the official line, quoted as-is, without a second endorsement from us.

And in the company's own 51-page technical report, there's a full comparison table attached. In it, V4.1 Flash is lower than V4 Pro on two items:

That asterisk needs spelling out: both 42.7 and 39.1 carry it, which usually indicates a "text-only subset" basis — meaning the two HLE numbers aren't a straight apples-to-apples comparison. We note the asterisk's existence because omitting it would make this a selective citation.

Where it genuinely leads by a wide margin is Agent and terminal-operation tasks:

So the more accurate description is: it greatly surpasses V4 Pro on Agent and terminal-operation tasks, and falls slightly behind on graduate-level reasoning tasks. And it is precisely with the phrase "comprehensively surpasses" that the company justifies the full routing on September 14 and the "orderly retirement of V4 Pro."

Three traps have to be called out.

Trap one: Terminal-Bench's version split. It scores 90.6 on 2.1 — the number the company put in its press release. But within the same benchmark family, it scores only 30.0 on 3.0 and 31.2 on 4.0 — behind Opus5's 43.3 and 39.9, and behind GPT5.6-Sol's 51.8. The old and new versions lead to opposite conclusions, so citing only one of them is half a fact.

Trap two: get DeepSWE's "0.2 points" right. V4.1-Flash scores 74.2; in the comparison table GPT5.6-Sol is 74.0 and Opus5 is 73.0. So the accurate statement is: it leads GPT-5.6 Sol by just 0.2 points, and Opus 5 by 1.2. It is not "a narrow 0.2-point win over Opus 5" — some secondhand accounts get that comparison wrong.

Trap three: whose benchmarks are these? The items where it scores high — DeepSWE, CyberGym, Automation-Bench — are all DeepSeek's own or partner benchmarks, not independent third-party leaderboards. That doesn't make the scores invalid, but there's currently no external audit.

2. Party One: The Architecture the Company Reports, and "Why It Looks Like This"

V4.1 Flash was released on September 10: a 552B-parameter MoE (backbone figure; there's also a 196B Engram conditional memory, so the total is higher than 552B).

The most important architectural change is an entirely new Causal-Encoder-Decoder (CED) structure: 40 layers = 20 causal encoder layers + 20 decoder layers. It produces asymmetric activation — 8 billion parameters activated per token in the prefill stage, 16 billion per token in the decode stage.

The stated design motivation is worth writing down: long-horizon Agent workloads are "input-heavy" — the volume of tool output, files, and history they read far exceeds what they generate — so the read and write stages are split apart to cut costs on each separately. That passage answers "why this model looks the way it does" — it isn't a general capability uplift, it's a trade-off custom-built for Agent workloads.

Other public facts: 384 routed experts plus 1 shared expert, 6 activated per token; maximum position encoding 1,048,576, i.e. a 1M context; FP4 main KV cache. Training ran from scratch on a 45-trillion-token multimodal corpus; sparse attention was first trained on 64K sequences, then the context was extended to 1 million at 34 trillion tokens; post-training used SFT, RL, and online distillation (OPD). On the vision side, DeepSeek-ViT is trained from scratch plus a two-layer MLP projector, processed jointly with text from the very start of pretraining — native multimodality, unlike the previous generation's bolt-on vision-exp approach. It also supports integer reasoning-effort adjustment from 1 to 100.

Those three KV cache numbers each use a different baseline, and they're the easiest thing to mix up:

• Relative to the previous generation, V4-Flash, HBM footprint drops to 1/4 and SSD footprint to 1/8;
• Relative to the first generation, V1 (November 2023), the reduction is 437×.

The evolution chain: V1 at 389,120 bytes/token, to 48,068 in V3.2, to 3,514 in V4-Flash, and then 890 in V4.1-Flash. At a million tokens, the active state is under 1GB.

A negative point also deserves a note: third parties have flagged deployment friction — there's no standard chat template, only a reference Python encoder and Rust tooling. Open source doesn't mean easy to get started with.

3. Party Two: Platform Measurements, the Coldest Set of Numbers

OpenRouter listed the model on September 10, priced at $0.30 per million input tokens, $1.20 per million output tokens, and $0.006 per million cache-read tokens. More notable are the production measurements: P50 latency 0.76 seconds, throughput 134 tokens/second.

That number has to be read alongside the beta-period feedback: during the beta, community speed figures were scattered across 284, 355, 420, and 507 tok/s, each with different sources and scenarios; in production, it's 134. The reason isn't hard to grasp: the beta capped accounts at 20 concurrent requests, so the road was basically empty; the release version's concurrency ceiling is 2500. Beta peaks are not production norms, and capacity planning off beta numbers will blow up in your face.

4. Party Three: Real Community Runs — the Most Human and the Most Contradictory

That developer from Sina ran the same model with the same hands on the same day and got two extremes.

The upside: a Minecraft-style voxel world (terrain, blocks, first-person view, inventory, lighting, water, survival loop) — the model worked a full 52 minutes and delivered a complete 7,550-line game, with all 53 Node.js unit tests and 38 browser integration tests passing. More interesting still, it reviewed its own code, found 15 bugs, fixed them one by one, and added regression tests.

The downside: the same developer tested a "monster city battle" and ran for over two hours with no result, then gave up.

There's a middle case too: a 3D pelican-riding-a-bike animation delivered a complete Three.js project (6 source files) in 12 minutes, but the frame clips through, the handlebars are missing, and the pelican's legs look like two pieces of bone stitched together — usable, with flaws.

On cost, including the unfinished tests, the total was 156 million tokens, 825 API calls, and $1.93 (about ¥13). His own verdict: "156 million tokens for ¥13 — still a pretty good deal."

Feedback on Reddit's r/LocalLLaMA reports roughly a 2.24× speedup in testing and up to about 30% better token efficiency; some also speculate that part of the speed gain comes from the beta's low concurrency rather than the architecture itself, and that doubt should be kept on the record.

The following is beta-build data; the release version has no repeat tests in the same scenarios:

• 3D S-shaped parking: V4.1 finished in 25.4 seconds with zero collisions; V4 Flash took 11 minutes 27 seconds and rendered the view upside down;
• Small shop task: 1 minute 41 seconds vs 5 minutes 54 seconds;
• 14 task sets: 34.5 minutes end to end vs 30.5 minutes — V4.1 was actually slower, with 18.7 minutes of that spent waiting on tools vs 6.6 minutes. Fast tokens don't mean fast delivery;
• Self-identity confusion: asked "who are you," it answered "Claude" (we found no dedicated verification of whether this was fixed after release);
• Video isn't supported yet; only images were tested.

On cost, this piece reaches no verdict. "156 million tokens for ¥13" and "¥10 in 5 minutes" involve task scales and cache hit rates that aren't comparable. Whether you really save money depends on your cache hit rate and your input-to-output ratio; some media also read this price cut as a remedial move after an earlier hike drove users away. Test it against your own task structure.

5. Party Four: Questions About Process and Motive

The notification process was far too rushed. The routing notice went out only as a single official group post on September 9 — no API changelog, no formal deprecation announcement — and less than 48 hours passed from notice to effect. By comparison, OpenAI gave six months' formal notice before shutting down the Sora API. One Hacker News comment is worth quoting as-is: "If you've validated workflows on V4 Pro, you may not want to suddenly be testing V4.1 Flash in production."

The version number is misleading. The company calls this "the smallest model in the new architecture family," yet gives it a ".1" version number. A model that opens an entirely new architecture family, and a model that only tweaks the post-training recipe, differ by a single decimal point in version numbering — developers have no way to judge from that how much evaluation budget to spend.

There's still no third-party audit. As of September 11, Artificial Analysis had published no measurements, and the model page still said "not yet deployed by any inference provider"; the model can't be found on LMArena, the official SWE-bench leaderboard, or LiveCodeBench either.

Put those three things together and you get one sentence: a Flash-tier model comprehensively surpassing its own flagship is a load-bearing claim being used to retire that flagship — and it currently has no external audit behind it.

There's also a practical risk: reproducibility. The old model names deepseek-v4-flash and deepseek-v4-flash-vision-exp have been taken offline, and requests are silently routed to V4.1 Flash; if a test suite still calls the old names, it may be running the new model.

The call name and the actual model name are two different levels — don't conflate them:

• The call name you put in a request is <code>deepseek-flash</code> — that's the name the company requires you to use;
• After server-side resolution, the model actually serving you is <code>deepseek-v4.1-flash</code>, and that's what the model field in the response returns;
• The official version string is written DeepSeek-V4.1-Flash.

So if you see deepseek-v4.1-flash in a response's model field, don't be confused — you didn't fill it in wrong; that's the result of the call name being resolved. The real risk is on the other end: test suites calling the old names deepseek-v4-flash or deepseek-v4-flash-vision-exp are in fact already running the new model.

6. DSH Users: This Time It's a Hard Binding — Our Readers Should Read This Section Most

DeepSeek Harness v0.1.5 shipped in step with V4.1 Flash, and the company did dedicated training optimization for this model inside DSH.

The official Harness update notes say, verbatim: "the new version is deeply integrated with DeepSeek V4.1 Flash model training; the model was specially trained and optimized across different DSH configurations" — covering three configurations: standard mode, programmatic tool calling (PTC) mode, and minimal mode.

The most valuable item is here: when using V4.1 Flash, the new DSH version can update the system prompt while preserving the existing KV Cache. For continuously running Agent tasks, that means you can change the task direction mid-flight without recomputing the entire context.

Read that alongside the architecture in section 2: CED splits reads from writes, KV cache is compressed to 890 bytes/token, and the active state at a million tokens is under 1GB — and what all that technical design lands as, in the product, is one feature: "changing the system prompt doesn't recompute the context." The technology isn't showing off; it ultimately lands as one action a user can feel — the most elegant closed loop in this piece.

Other updates: the web interface supports uploading images and PDFs; the Sidebar provides a file tree and previews (Markdown, HTML, PDF, code, images); standardized plugin extension entry points open on both sides; multi-Agent support includes parent-child two-way communication and interrupt-to-stop, the main Agent can pick the model and reasoning effort for sub-agents, plus an experimental Agent Teams (shared task list, off by default). Installation is npx @deepseek-ai/dsh web.

Distinguish the identity: DSH is DeepSeek's own open-source Agent framework and is not on the official partner list (that label goes to third-party products). But V4.1 Flash is the first model with dedicated training optimization across multiple Harness configurations, which is a much harder binding than a partner list.

7. Specs, Prices, and a Few Facts That Are Easy to Get Wrong

Specs: 1M context, 384K output cap, 2500 concurrency (V4 Pro is 500), image understanding supported (V4 Pro doesn't support it), thinking mode on by default and switchable.

Prices (yuan/million tokens, effective 12:00 on September 10):

Peak hours are Monday–Friday 9:00–12:00 and 14:00–18:00 Beijing time, everything else is off-peak, and off-peak prices are half of peak. Note: V4 Pro's prices did not change this time. The -60%, -33%, and -11% we reported earlier are Flash's new prices versus Flash's old prices, not versus Pro.

License: multiple tech media report an MIT license, but we could not directly verify the LICENSE file in the repository, so we can only write "multiple tech media report an MIT license; before commercial use, defer to the LICENSE file in the repository" — we don't recommend inferring unlimited commercial use from it.

Multimodality: the official line covers only vision and image understanding. Some third parties claim audio is included, with no official basis; this article doesn't adopt it.

V4.1 Pro launch timing: not announced officially; this article makes no guesses.

8. So How Should You Use It?

First, long-running Agent tasks are worth serious evaluation. Architecture, benchmarks, and community tests all point the same way: the read/write asymmetric design was built for exactly this, and the jumps on Terminal-Bench 2.1, Automation-Bench, and Codeforces aren't noise.

Second, don't rush to switch for graduate-level reasoning. On GPQA Diamond and HLE, the company's own table is right there, and the scores are lower.

Third, discount every benchmark score and wait for your own results. The gap between 134 tok/s production throughput and beta peaks, the missing third-party audit, and the reproducibility risk from silent routing are all real friction.

Finally, back to the opening: after 12:00 on September 14, V4 Pro requests are fully routed to V4.1 Flash and billed at Flash prices, until V4.1 Pro launches. Whether or not you accept "comprehensively surpasses," the timing is certain — before then, run your single most critical workflow through it once.

Data and Sourcing Notes

Data in this article was collected on September 11, 2026; the model was released on September 10; the routing takes effect at 12:00 on September 14 (Beijing time). Official figures come from the pricing-page note, the news page, the changelog, and the technical report; platform measurements come from OpenRouter's listing; community tests come from developers' public posts and Reddit discussions. Beta-period data is labeled "beta build" and isn't mixed with release-version data; competitor scores are stated as-is; "comprehensively surpasses V4 Pro" is the official line, and this article doesn't second it.