DeepSeek V4.1 Flash 1017 lifts Terminal-Bench 4.0 from 31.2 to 42.3
V4.1 Flash 1017 released with MIT open weights
DeepSeek released V4.1 Flash 1017, a new checkpoint of V4.1 Flash, on October 17, 2026. DeepSeek put the repo and the weights under the MIT license. You can download them from Hugging Face and ModelScope.
Source: DeepSeek announcement, Oct 17 ↗
Terminal-Bench 4.0 rises from 31.2 to 42.3
In DeepSeek's announcement table, Terminal-Bench 4.0 rose from 31.2 for the previous version to 42.3. All 11 benchmarks in the table went up, and Terminal-Bench gained the most. DeepSeek ran every one of them itself.
Source: DeepSeek announcement table ↗
Artificial Analysis Intelligence Index rises from 39 to 45
Artificial Analysis scores 1017 at 45, six points above the previous version's 39. Among open-weights models, only MiMo-V2.6-Pro scores higher, at 46. GLM-5.3 ties 1017 at 45.
Source: Artificial Analysis model page ↗
Hallucination rate falls from 96% to 67%
The AA-Omniscience hallucination rate fell from 96% to 67%, a 29 point drop. Lower is better. Gemini 4 Argon (high) sits at 15%.
Source: AA-Omniscience ↗
deepseek-flash in the API now points to 1017
DeepSeek pointed the API model name deepseek-flash at 1017, so your calling code stays the same. 1017 also answers requests sent to the old names deepseek-v4-flash and deepseek-v4-flash-vision-exp.
Source: DeepSeek API docs ↗
DeepSeek shipped V4.1 Flash 1017, a new checkpoint of V4.1 Flash, on October 17. DeepSeek calls it a "minor update", yet all 11 benchmarks in its table went up. Terminal-Bench 4.0 jumped the most, from 31.2 to 42.3. Artificial Analysis scores it 45, up from 39. That ties GLM-5.3 for second among open-weights models. Its hallucination rate fell from 96% to 67%. Most of the models in the comparison still score lower.
What shipped
- DeepSeek released it on October 17, 2026. The announcement page gives only the date. DeepSeek's post on X went up at 17:35 KST (08:35 UTC). DeepSeek created the Hugging Face repo at 11:40 KST (02:40 UTC) the same day. (DeepSeek announcement, X post)
- DeepSeek says the model scores higher, finishes tasks faster and writes shorter outputs than the previous version. DeepSeek used reinforcement learning with self-feedback to improve building whole projects from scratch, such as games and 3D scenes. It tuned the model for DeepSeek Harness, Tencent CodeBuddy and WorkBuddy. The company recommends DeepSeek Harness v0.2.1-rc.1 or later.
- It is a mixture-of-experts model that takes images and text as input. The backbone has 552B parameters. Each token uses 8B active parameters during prefill and 16B during decode. A separate 196B Engram memory brings the weight total shown on Hugging Face to about 763B. Context runs to 1M tokens, and the API caps output at 384K tokens. The original V4.1 Flash card describes the architecture. (V4.1 Flash model card)
- The repo and the weights are MIT licensed. You can download them from Hugging Face and ModelScope.
- The API model name
deepseek-flashnow points to 1017. Your calling code stays the same. 1017 also answers requests sent to the old namesdeepseek-v4-flashanddeepseek-v4-flash-vision-exp. (API docs) - DeepSeek also opened its Search and Fetch APIs to everyone the same day. Both run on DeepSeek's own search engine.
Official benchmarks
| Official benchmarks vs. the previous version | V4.1 Flash 1017 | V4.1 Flash (previous) |
|---|---|---|
| Terminal-Bench 4.0 | 42.3 | 31.2 |
| ProgramBench | 29.1 | 20.3 |
| Apex Bench v1.1 | 72.1 | 64.6 |
| HLE (no tools) | 42.6 | 36.8 |
| Automation-Bench | 60.5 | 54.8 |
| DeepSWE v1.1 | 77.0 | 74.2 |
| Benchmark | Previous | 1017 | Change |
|---|---|---|---|
| Terminal-Bench 4.0 | 31.2 | 42.3 | +11.1 |
| ProgramBench | 20.3 | 29.1 | +8.8 |
| Apex Bench v1.1 | 64.6 | 72.1 | +7.5 |
| HLE (no tools) | 36.8 | 42.6 | +5.8 |
| Automation-Bench v1.0.6 | 54.8 | 60.5 | +5.7 |
| Chartography (with tools) | 78.9 | 83.0 | +4.1 |
| Agents' Last Exam | 31.8 | 35.4 | +3.6 |
| BabyVision (with tools) | 89.6 | 92.5 | +2.9 |
| DeepSWE v1.1 | 74.2 | 77.0 | +2.8 |
| NL2Repo-Bench | 65.4 | 68.2 | +2.8 |
| ZeroBench-main (with tools) | 49.0 | 51.0 | +2.0 |
Source: DeepSeek announcement table, Hugging Face model card. The same table includes Kimi K3, GLM-5.3, GPT-5.6 Sol and Claude Opus 5. Of the six models, 1017 leads on four benchmarks: 77.0 on DeepSWE v1.1, 60.5 on Automation-Bench, 35.4 on Agents' Last Exam and 72.1 on Apex Bench. Opus 5 leads six of the other seven, and GPT-5.6 Sol leads ZeroBench. On Terminal-Bench 4.0, Opus 5 is highest at 51.8.
- DeepSeek picked the comparison models. It left newer models such as Claude Opus 5.5 and GPT-6.1 Sol out of the table.
- DeepSeek ran Terminal-Bench 4.0 with the Claude Code harness and DeepSWE v1.1 with the Mini-SWE-V2 harness. The original V4.1 Flash card says the previous version's 31.2 on Terminal-Bench 4.0 came from DeepSeek Harness.
- The previous version's NL2Repo-Bench score is 64.0 on the original card and 65.4 in the 1017 table. I use the 1017 table here.
- On the text-only HLE subset, the score went from 39.1 to 44.0.
Artificial Analysis Intelligence Index
| Artificial Analysis Intelligence Index | Artificial Analysis Intelligence Index |
|---|---|
| Claude Opus 5.5 (max) | 58 |
| Claude Sonnet 5.5 (max) | 56 |
| Muse Wonder Full (max) | 56 |
| GPT-6 Astra (max) | 53 |
| Gemini 4 Argon (high) | 53 |
| GPT-6.1 Sol (max) | 52 |
| Grok 4.7 (xhigh) | 46 |
| MiMo-V2.6-Pro | 46 |
| Qwen3.8 Max | 45 |
| DeepSeek V4.1 Flash 1017 (max) | 45 |
| GLM-5.3 (max) | 45 |
| Kimi K3 (max) | 44 |
| Claude Haiku 5.5 (max) | 43 |
| Gemini 3.8 Flash (high) | 41 |
| DeepSeek V4.1 Flash (max, previous) | 39 |
Artificial Analysis scores 1017 at 45, six points above the previous version. Among open-weights models, only MiMo-V2.6-Pro scores higher, at 46. GLM-5.3 ties 1017 at 45. Step 5 Preview and Kimi K3 score 44. In the same release, its GDPval-AA v2.1 Elo rose from 1600 to 1746 and its AA-Briefcase v1.1 Elo from 1420 to 1570. CritPt went from 14% to 24%. 1017 uses 89K output tokens per index task on average. The Artificial Analysis model page describes it as very verbose. (Artificial Analysis post, model page)
Hallucination rate
| AA-Omniscience hallucination rate (%) | % |
|---|---|
| Gemini 4 Argon (high) | 15% |
| Grok 4.7 (xhigh) | 29% |
| GLM-5.3 (max) | 30% |
| Kimi K3 (max) | 53% |
| GPT-6.1 Sol (max) | 54% |
| Claude Opus 5.5 (max) | 59% |
| DeepSeek V4.1 Flash 1017 (max) | 67% |
| DeepSeek V4.1 Flash (max, previous) | 96% |
Artificial Analysis computes the hallucination rate as wrong answers divided by wrong answers, partial answers and skipped questions combined. Of the questions a model misses, it is the share where the model gives a wrong answer instead of saying it does not know. Lower is better. 1017 dropped from 96% to 67%, a 29 point fall. Its AA-Omniscience Index went from -5 to +5. Among the models above, only the previous version is higher. (AA-Omniscience, Artificial Analysis post)
Community reactions
The comments below come from the Hacker News launch thread and two r/LocalLLaMA threads, the launch thread and a ranking thread. All numbers are the commenters' own claims.
- Speed and price make it feel better. One commenter said it trails GPT Sol and Gemini on benchmarks but is pleasant to use. It responds fast, shows its reasoning and never gets blocked by a cyber review. (HN @OsrsNeedsf2P)
- It uses fewer tokens. Compared with the old weights, one commenter found reasoning effort 1 now usable, with 2.5x fewer tokens. Tokens dropped about 20% at every effort level, while intelligence and the tool-calling pattern stayed the same. (HN @apejcic)
- It answers faster. One measurement on Fireworks put a quality score up from 3.46 to 3.77. Median response time fell from 16.8 to 8.0 seconds. The settings differed, though: 1017 ran with medium reasoning and the previous version with low. (Reddit u/mvaranka)
- You notice the improvement right away. One user called it clearly better than the original V4.1 Flash and ahead of Qwen3.8-Flash-Next in some areas. (Reddit u/sixx7)
- Some found it worse. On a personal benchmark built from historical PR reviews, one commenter said it took 30% longer than the previous version and did worse. (HN @springtimesun)
- It talks a lot and goes in circles. One user went back to GLM 5.3 Flash because it kept going in circles. Another said its reasoning seemed to go on forever. (Reddit u/progbeercode, u/challis88ocarina)
- It is hard to run at home. One user could barely run the earlier 0731 release at home and called this size practically out of reach. People asked for a smaller model. (Reddit u/daank, HN @jononor)
- The benchmark choice flatters it. One commenter said DeepSeek tilts the comparison just by choosing which benchmarks to publish. Another poked at calling it a "minor update" with "substantial improvements". (Reddit u/SexyAlienHotTubWater, HN @pmxi)
Usage examples from X
All of these went up after the official announcement on October 17. Times and results are the posters' own claims.
The author gave four models the same prompt for an Eiffel Tower and Paris 3D scene. 1017 took 1h 10m 31s, Claude Haiku 5.5 45m 02s, Gemini 4 Argon 38m 22s and Muse Spark 1.3 17m 01s. The author called Argon's version the most polished.
DeepSeek V4.1 Flash 1017 vs Gemini 4 Argon vs Muse Spark 1.3 vs Claude Haiku 5.5 The famous Eiffel Tower benchmark is back! 🗼🇫🇷 Same prompt for all four. • DeepSeek V4.1 Flash 1017: $0.53, 1h 10m 31s • Muse Spark 1.3: Free, 17m 01s • Claude Haiku 5.5: $0.43, 45m 02s • Show more
The author compared two harnesses on the deepseek-flash API, running 34 coding, shell and git tasks twice each. Both passed 407 of 408. DeepSeek Harness took 1.63x the time and 1.91x the output tokens of the minimal Pi harness.
DeepSeek says V4.1-Flash-1017 was trained for their own harness, so I tested it. Same model (deepseek-flash API, thinking high), same 34 coding/shell/git tasks, 2 runs each, interleaved. Quality tied (407/408 passed). But DeepSeek Harness took 1.63x the time and 1.91x the Show more
The author ran a simple task with the same prompt on 1017 and Claude Opus 5.5. 1017 with max thinking took 47 minutes and Opus 5.5 took 44. The author preferred how the Opus result looked.
Claude Opus 5.5 vs DeepSeek V4.1 Flash 1017 max thinking One more test, same promt, simple task > DeepSeek V4.1 Flash 1017 took 47 mins and cost $1.4 > Claude Opus 5.5 took 44 mins and cost $9.2 Visually, Claude Opus 5.5 is my fav.
Everyone said DeepSeek V4.1 kills Opus 5.5 Same promt, simple task: "Show how Mac mini works" Despite the price I still go with Claude Opus 5.5 What matters more to you, price or quality of the finished task?
The author built a Three.js gummy bear scene in one shot. It took 2 hours 3 minutes. The author called the result impressive but slow to arrive.
DeepSeek V4.1 Flash-1017 built Gummy Bears in one shot 😳 - built with Three.js - time taken: 2 hours 3 minutes - total cost: $0.59 the result is impressive but it took quite a while to build
Still unclear
- DeepSeek's announcement page does not give an exact release time. The times above come from the X post and from when the Hugging Face repo was created.
- The Artificial Analysis post says AA-Omniscience accuracy went up, but the chart in the same post does not match. I left accuracy out for that reason.
- DeepSeek ran all the official benchmarks itself.