What Do the DeepSeek V4.1 Flash Benchmarks Actually Show in 2026?
DeepSeek's model card for V4.1 Flash publishes a comparison against Claude Opus 5.0, GPT-5.6 Sol, Kimi K3, GLM-5.3, DeepSeek V4 Pro and DeepSeek V4 Flash, all at maximum reasoning effort, across reasoning, agentic and visual-agent benchmarks. The pattern is unusually legible. V4.1 Flash wins the established agentic benchmarks and the business-workflow benchmarks, and it loses the newest and hardest agentic benchmarks by wide margins. Every figure is DeepSeek's own, run in DeepSeek's framework, and the V4.1 Flash overview covers the model itself.
First, the comparators are previous-generation: Claude Fable 5.1 and GPT-6 Astra both shipped in September 2026 and do not appear. Second, DeepSeek's scores use reasoning effort 100, its maximum; production callers will usually run lower. Third, several rows use different agent scaffolds for different models, and DeepSeek's own scaffold table shows a five-point swing on the same model. Read every number as vendor-reported and configuration-dependent.
Where Does DeepSeek V4.1 Flash Lead in 2026?
DeepSeek V4.1 Flash posts the top score in its own table on seven rows, and they cluster around agentic coding on established benchmarks, business workflow automation, cybersecurity tasks and competitive programming. These are the rows most relevant to a team evaluating the model for operational work rather than for open-ended research.
| Benchmark (vendor-reported, max effort) | DS V4.1 Flash | Claude Opus 5 | GPT-5.6 Sol | GLM-5.3 | DS V4 Pro | What it broadly measures |
|---|---|---|---|---|---|---|
| Terminal-Bench 2.1 | 90.6 | 89.1 | 88.8 | 88.2 | 87.9 | Agentic terminal tasks, established set |
| DeepSWE v1.1 | 74.2 | 74.0 | 73.0 | 66.9 | 62.7 | Long-horizon software engineering |
| AutomationBench | 54.8 | 50.3 | 45.8 | 48.8 | 43.2 | Multi-step business workflows |
| Agent's Last Exam | 31.8 | 28.6 | 26.7 | 28.5 | 25.7 | Long professional tasks in real software |
| CyberGym | 88.1 | not listed | 84.5 | 84.5 | 83.3 | Cybersecurity tasks |
| HLE with tools | 63.9 | 63.6 | not listed | 62.5 | 60.0 | Expert questions with tool access |
| Codeforces (rating) | 3471 | not listed | not listed | not listed | 3348 | Competitive programming |
| MathArena Apex | 65.6 | not listed | not listed | not listed | 65.3 | Competition mathematics |
Two of these rows matter most to a business reader. AutomationBench is the closest public proxy to a marketing operations workflow, and 54.8 is the highest score in the table by 4.5 points. It is also a score that fails 45 percent of tasks, which is a reason to keep the human checkpoint, not to remove it. Agent's Last Exam measures long professional tasks in real software, and 31.8 leads the table while still failing more than two thirds of tasks. The lead is real; the absolute level says the category is hard for everyone in 2026.
The Terminal-Bench 2.1 and DeepSWE rows are the ones DeepSeek's marketing leans on, and they deserve a second look. The margin over Opus 5 is 1.5 points on one and 0.2 on the other. Within DeepSeek's own scaffold table, the same model moves by more than that depending on the harness. These are ties, not wins, and a tie at a fortieth of the price is still the important fact.
Where Does DeepSeek V4.1 Flash Trail in 2026?
DeepSeek V4.1 Flash trails the previous-generation flagships by wide margins on the newest and hardest agentic benchmarks and on expert reasoning without tools. DeepSeek publishes these rows rather than omitting them, which is to its credit, and they are the rows a team should read before routing anything open-ended to the model.
| Benchmark (vendor-reported, max effort) | DS V4.1 Flash | Best in table | Gap | What it broadly measures |
|---|---|---|---|---|
| Terminal-Bench 3.0 | 30.0 | 43.3 (Opus 5) | 13.3 points | Harder terminal agent tasks |
| Terminal-Bench 4.0 | 31.2 | 51.8 (Opus 5) | 20.6 points | Hardest terminal agent tasks |
| ProgramBench | 20.3 | 37.0 (Opus 5) | 16.7 points | Whole-program construction |
| NL2Repo-Bench | 64.0 | 75.3 (Opus 5) | 11.3 points | Natural language to repository |
| SEC-Bench Pro | 62.8 | 74.3 (GPT-5.6 Sol) | 11.5 points | Security engineering |
| ExploitGym | 15.3 | 33.7 (GPT-5.6 Sol) | 18.4 points | Exploit development |
| HLE, no tools | 36.8 | 56.3 (Opus 5) | 19.5 points | Expert reasoning unaided |
| GPQA Diamond | 90.9 | 94.1 (GPT-5.6 Sol) | 3.2 points | Graduate science reasoning |
| Chartography with tools | 78.9 | 84.0 (Opus 5) | 5.1 points | Chart reasoning, visual agent |
| BabyVision with tools | 89.6 | 94.1 (Opus 5) | 4.5 points | Visual reasoning, visual agent |
The shape is consistent. On Terminal-Bench, V4.1 Flash goes from leading version 2.1 to trailing version 3.0 by 13 points and version 4.0 by 21. The newer versions are built to be harder and less contaminated, and DeepSeek's model card notes its post-training relies on large-scale automated synthesis of agent tasks and environments. A model trained heavily on synthetic agent tasks that resemble established benchmarks would be expected to show exactly this profile: excellent on the known distribution, weaker on the new one. That is an inference, not something DeepSeek states, and it is the inference a careful buyer should test.
For context, OpenAI's GPT-6 Astra launch post, also from September 2026, reports 57.9 on Terminal-Bench 4.0, and Anthropic reports Claude Fable 5.1 at 55.8. Those are different vendors' runs under different settings and not directly comparable with DeepSeek's 31.2, but the direction is clear: on the hardest current agentic benchmark, the September 2026 frontier is roughly 25 points ahead of V4.1 Flash.
Why Does the Agent Scaffold Change DeepSeek's Scores in 2026?
Because the scaffold is part of the system being measured. DeepSeek published a second table showing V4.1 Flash on the same two benchmarks across eight scaffolds, and the spread is the most useful thing on the model card for anyone building agents. On DeepSWE v1.1 the model scores 74.2 on mini-SWE and 69.8 on Claude Code. On Terminal-Bench 2.1 it scores 90.6 on DeepSeek's own minimal harness and 84.1 on Codex.
| Scaffold (vendor-reported, max effort) | DeepSWE v1.1 | Terminal-Bench 2.1 |
|---|---|---|
| mini-SWE | 74.2 | 90.3 |
| DeepSeek Harness, Minimal | 72.6 | 90.6 |
| DeepSeek Harness, Standard | 70.5 | 85.8 |
| Claude Code | 69.8 | 88.0 |
| DeepSeek Harness, PTC | 67.6 | 85.8 |
| Pi | 66.2 | 86.1 |
| Codex | 65.6 | 84.1 |
| OpenCode | 65.5 | 85.0 |
The headline 74.2 comes from the scaffold that suits the model best. On the two most widely used third-party coding harnesses, Claude Code and Codex, the same model scores 69.8 and 65.6. That still beats DeepSeek V4 Pro's 62.7, but it no longer ties Opus 5 at 74.0. The lesson for 2026 is general: a benchmark number without the scaffold named is half a number, and a model that leads on one harness may sit mid-table on the one your team actually uses.
How Does the Base Model Compare to V4 Pro in 2026?
DeepSeek also publishes a base-model comparison before post-training, which is the fairest way to see what the architecture and pre-training deliver on their own. V4.1 Flash's base model, with 8B and 16B active parameters, beats the 49B-active V4 Pro base on MMLU-Pro (74.1 versus 73.5), BigCodeBench (60.6 versus 59.2), HumanEval (79.4 versus 76.8) and GSM8K (93.0 versus 92.6). It trails V4 Pro on knowledge-heavy rows: SimpleQA-Verified (42.3 versus 55.2), MultiLoKo (45.5 versus 50.9) and LongBench-V2 (45.2 versus 51.5). A smaller active model knowing fewer facts is expected; the multimodal rows, including DocVQA at 95.6, have no V4 Pro comparison because V4 Pro has no vision.
How Should Business Teams Read the DeepSeek Table in 2026?
As a map of what to route where. The rows V4.1 Flash wins describe well-specified, multi-step operational work: a defined task, defined tools, a verifiable end state. The rows it loses describe open-ended construction and unaided expert reasoning. That maps cleanly onto a marketing stack, and the marketing team guide works through the fit in detail.
| Task type | Benchmark evidence in 2026 | Routing implication |
|---|---|---|
| Defined multi-step workflows with tools | Leads AutomationBench and Agent's Last Exam | Strong candidate at a fortieth of frontier price, with a human checkpoint. |
| Established coding and repo tasks | Ties or leads on Terminal-Bench 2.1 and DeepSWE, scaffold-dependent | Test on your own harness before assuming the headline number. |
| Open-ended, novel agentic tasks | Trails by 13 to 21 points on Terminal-Bench 3.0 and 4.0, 17 on ProgramBench | Keep on a frontier tier. |
| Expert reasoning without tools | Trails HLE by 19.5 points | Not the model for unaided analysis. |
| Expert reasoning with tools | Leads HLE with tools narrowly | Give it search and tools and it competes. |
| Document and image understanding | DocVQA 95.6; visual agent rows within 5 points of Opus 5 | Good fit for receipts, PDFs and creative review at the price. |
What Are the Common Mistakes in Reading These Benchmarks in 2026?
- Quoting the table against Fable 5.1 or GPT-6 Astra. Neither is in it. The comparators are Opus 5 and GPT-5.6 Sol, both replaced the same month.
- Reading a 0.2-point DeepSWE margin as a win. Scaffold choice moves the score by 8.7 points on the same benchmark.
- Ignoring the version number on Terminal-Bench. Leading 2.1 and trailing 4.0 by 21 points are both true, and the second matters more for new work.
- Assuming production runs at effort 100. Every instruct score is at maximum effort. Your cost-optimised setting will score lower.
- Treating AutomationBench 54.8 as automation solved. It fails 45 percent of tasks. Keep the checkpoint.
- Skipping your own evaluation set. Twenty to fifty real tasks with known good outputs settle every question this table raises, in an afternoon.
Key Takeaways for 2026
- DeepSeek V4.1 Flash leads its own table on Terminal-Bench 2.1, DeepSWE, AutomationBench, Agent's Last Exam, CyberGym, HLE with tools and Codeforces.
- It trails Opus 5 and GPT-5.6 Sol by 11 to 21 points on Terminal-Bench 3.0 and 4.0, ProgramBench, NL2Repo, SEC-Bench Pro, ExploitGym and unaided HLE.
- The comparators were superseded by Claude Fable 5.1 and GPT-6 Astra in the same month; those vendors report roughly 25 points more on Terminal-Bench 4.0.
- Scaffold choice swings DeepSWE from 74.2 to 65.6 and Terminal-Bench 2.1 from 90.6 to 84.1 on the same model.
- All scores are vendor-run at reasoning effort 100.
- Routing conclusion: defined multi-step operational work and document understanding, yes; open-ended construction and unaided expert reasoning, keep on a frontier tier.
Distk builds the evaluation set that turns a vendor table into a routing decision for growth teams across India and internationally, then wires the winning model per workflow into the stack with the checkpoints in place. If DeepSeek's September 2026 numbers are on your desk, that evaluation is where we start.