DeepSeek V4.1 Flash 1017 vs GPT-6.1 Terra
Full profiles: DeepSeek V4.1 Flash 1017 (DeepSeek) and GPT-6.1 Terra (OpenAI): every benchmark, price and rank.
Updated · Data from 7 sources
As of 27 Oct 2026, DeepSeek V4.1 Flash 1017 (max) and GPT-6.1 Terra (max) are neck and neck on quality (63.9 vs 65.9 on the BenchLeader Index, inside the ±8.0 margin of error), and DeepSeek V4.1 Flash 1017 (max) costs 3.8× less, which makes it the better buy for most workloads. GPT-6.1 Terra leads in every category we measure both in.
Which to pick for…
The pick's numbers come first. Category scores on the BenchLeader scale (50 is the average model, 15 points one standard deviation). High confidence means a lead of half a standard deviation or more; under one point is called either.
| Use case | Pick | Why | Confidence |
|---|---|---|---|
| Reasoning | GPT-6.1 Terra | 86 vs 70 category score; CritPt 28.0% vs 24.0% | High |
| Knowledge | GPT-6.1 Terra | 75 vs 57 category score; AA-Omniscience 29.8 vs 4.7 | High |
| Long documents | Either | 66 vs 66 category score; AA-LCR 82.7% vs 82.7% | Either |
| Images and other media | GPT-6.1 Terra | 68 vs 63 category score; MMMU-Pro 84.7% vs 80.6% | Medium |
| Lowest cost | DeepSeek V4.1 Flash 1017 | $0.525 vs $2.00 per 1M blended; agentic coding session $0.023 vs $0.080 | – |
| Fastest first answer | DeepSeek V4.1 Flash 1017 | 13.0 s (DeepSeek V4.1 Flash 1017 (max)) vs 131.8 s (GPT-6.1 Terra (max)) to the first answer token | – |
| High volume on a budget | Neither flagship | A smaller model is usually the better fit: Cheapest good LLMs, Fastest LLMs | – |
Key differences
- DeepSeek V4.1 Flash 1017 (max) and GPT-6.1 Terra (max) are neck and neck on quality (63.9 vs 65.9, within the ±8.0 margin).
- DeepSeek V4.1 Flash 1017 (max) is stronger in agents & tools, coding, maths.
- GPT-6.1 Terra (max) is stronger in composite, knowledge, multimodal, reasoning.
- DeepSeek V4.1 Flash 1017 (max) is 3.8× cheaper ($0.525 vs $2.00 per 1M blended).
- DeepSeek V4.1 Flash 1017 (max) streams 1.9× faster (169 vs 91 tokens per second).
At a glance
| Spec | DeepSeek V4.1 Flash 1017 | GPT-6.1 Terra |
|---|---|---|
| Developer | DeepSeek | OpenAI |
| Released | 17 Oct 2026 | 26 Oct 2026 |
| Context window (tokens) | 1,048,576 | 1,050,000 |
| Compared at | DeepSeek V4.1 Flash 1017 (max) | GPT-6.1 Terra (max) |
| Open weights | Yes | No |
| Price in / out / cached input per 1M | $0.300 / $1.20 / $0.0060 | $1.00 / $5.00 / $0.050 |
Category by category
Category scores on the BenchLeader scale: 50 is the average evaluated model, 15 points is one standard deviation. A gap under one point is called level.
| Category | DeepSeek V4.1 Flash 1017 (max) | GPT-6.1 Terra (max) | Leader |
|---|---|---|---|
| Reasoning | 70.4 | 86.2 | GPT-6.1 Terra |
| Coding | 69.9 | – | not comparable |
| Agents & tools | 63.5 | – | not comparable |
| Maths | 61.8 | – | not comparable |
| Knowledge | 57.0 | 74.5 | GPT-6.1 Terra |
| Multimodal | 63.4 | 67.5 | GPT-6.1 Terra |
| Long context | 66.3 | 66.3 | level |
| Composite | 79.4 | 82.8 | GPT-6.1 Terra |
Where sources disagree
Across the 14 benchmarks both have, DeepSeek V4.1 Flash 1017 leads 4, GPT-6.1 Terra leads 9 and 1 is a tie. Every overall index that rates both puts GPT-6.1 Terra ahead.
| Overall index | DeepSeek V4.1 Flash 1017 (max) | GPT-6.1 Terra (max) | Leader |
|---|---|---|---|
| BenchLeader Index | 63.9 | 65.9 | neck and neck |
| AA Intelligence Index v4.3.2 | 45.1 | 48.1 | GPT-6.1 Terra |
Each publisher runs its own tasks, harnesses and effort settings, so their overall verdicts can differ; BenchLeader reconciles them on one scale. † measured at another effort setting of the same model (the publisher did not test the one compared here).
Price, speed and latency
List prices blended at three input tokens per output token. Per-task prices use the token counts on the methodology page; suite cost is what Artificial Analysis paid to run its whole Intelligence Index.
| Measure | DeepSeek V4.1 Flash 1017 (max) | GPT-6.1 Terra (max) |
|---|---|---|
| Blended price per 1M tokens | $0.525 | $2.00 |
| Input / output per 1M | $0.300 / $1.20 | $1.00 / $5.00 |
| Cached input per 1M | $0.0060 | $0.050 |
| Price per task: chat reply | $0.0005 | $0.0019 |
| Price per task: summarize a 30-page report | $0.0043 | $0.015 |
| Price per task: code edit | $0.0036 | $0.013 |
| Price per task: agentic coding session | $0.023 | $0.080 |
| Price per task: structured extraction | $0.0008 | $0.0030 |
| Cost to run a full benchmark suite | $0.262 | $0.403 |
| Output speed | 169 tok/s | 91 tok/s |
| Time to first answer | 13.0 s | 131.8 s |
| Time to a complete answer | 16.0 s | 137.3 s |
Per token is not per task: on Artificial Analysis' full suite DeepSeek V4.1 Flash 1017 (max) cost $0.262 and GPT-6.1 Terra (max) $0.403, because DeepSeek V4.1 Flash 1017 (max) used about 2.5× as many tokens for the same work, which a per-token price hides.
Each model is compared at its best-ranked reasoning-effort setting. DeepSeek V4.1 Flash 1017 (max) scores 63.9 and takes 13 s to its first answer token. GPT-6.1 Terra (max) scores 65.9 and takes 132 s to its first answer token; GPT-6.1 Terra (xhigh) scores 65.0 in 18 s, GPT-6.1 Terra (high) scores 64.7 in 7 s. Higher effort means more thinking before the answer: better scores, longer waits and more tokens billed. How reasoning effort changes scores and cost.
Every benchmark
57 benchmarks, grouped by category; each name links to the board and its leaders. † measured at another effort setting of the same model, shown for reference and not counted as a win.
| Metric | Publisher | DeepSeek V4.1 Flash 1017 (max) | GPT-6.1 Terra (max) |
|---|---|---|---|
| BenchLeader Index | BenchLeader | 63.9 | 65.9 |
| Agents & tools score | BenchLeader | 63.5 | – |
| Coding score | BenchLeader | 69.9 | – |
| Composite score | BenchLeader | 79.4 | 82.8 |
| Knowledge score | BenchLeader | 57.0 | 74.5 |
| Long context score | BenchLeader | 66.3 | 66.3 |
| Maths score | BenchLeader | 61.8 | – |
| Multimodal score | BenchLeader | 63.4 | 67.5 |
| Reasoning score | BenchLeader | 70.4 | 86.2 |
| Blended price $/M | BenchLeader | $0.525 | $2.00 |
| Output speed | BenchLeader | 169 tok/s | 91 tok/s |
| Time to first answer | BenchLeader | 13.0 s | 131.8 s |
| Context window | BenchLeader | 1,048,576 tokens | 1,050,000 tokens |
| Reasoning | |||
| GPQA Diamond | Epoch AI Benchmarking Hub | 93.2% | – |
| LiveBench 2.0 Reasoning | LiveBench | 89.1%† | 88.7%† |
| Humanity's Last Exam (AA) | Artificial Analysis | 43.3% | 46.3% |
| ARC-AGI-1 | ARC Prize | 93.5% | – |
| ARC-AGI-2 | ARC Prize | 78.1% | – |
| CritPt | Artificial Analysis | 24.0% | 28.0% |
| Chess Puzzles | Epoch AI Benchmarking Hub | 46.0% | – |
| Mystery Game Puzzles | Epoch AI Benchmarking Hub | 49.0% | – |
| Coding | |||
| Arena WebDev | Arena (LMArena) | 1665 | – |
| LiveBench 2.0 Coding | LiveBench | 96.0%† | 90.8%† |
| SciCode (AA) | Artificial Analysis | 55.9% | 55.1% |
| Code Migration | Vals AI | 44.2% | – |
| Code Migration v1.1 | Vals AI | 67.5% | – |
| Vibe Code Bench v1.1 | Vals AI | 87.7% | – |
| Terminal-Bench 4.0 (AA) | Artificial Analysis | 33.8% | 55.6% |
| FrontierCode 1.1 (AA) | Artificial Analysis | 43.7% | 36.0% |
| Agents & tools | |||
| LiveBench Long Agentic | LiveBench | 40.9%† | 48.6%† |
| LiveBench Browser | LiveBench | 67.2%† | 93.1%† |
| GDPval-AA v2.1 | Artificial Analysis | 62.3% | – |
| Finance Agent v2 | Vals AI | 57.0% | – |
| Finance Agent v2.1 | Vals AI | 62.9% | – |
| Harvey's Legal Agent Benchmark v1.1 | Vals AI | 14.2% | – |
| Harvey's Legal Agent Benchmark v1 | Vals AI | 9.2% | – |
| Legal Research Bench | Vals AI | 45.2% | – |
| Tax Agent Bench | Vals AI | 44.4% | – |
Show all 57 benchmarks
| Metric | Publisher | DeepSeek V4.1 Flash 1017 (max) | GPT-6.1 Terra (max) |
|---|---|---|---|
| Agents & tools | |||
| Terminal-Bench 4.0 (Vals) | Vals AI | 31.8% | – |
| AutomationBench | Artificial Analysis | 66.6% | 60.2% |
| GDP.pdf (AA) | Artificial Analysis | 17.2% | 28.6% |
| AA-Briefcase v1.1 | Artificial Analysis | 1570 | 1402 |
| Terminal-Bench-Science 0.1 (AA) | Artificial Analysis | 12.9% | 57.6% |
| CWE-Bench (AA) | Artificial Analysis | 69.2% | – |
| DeepSec-Bench (AA) | Artificial Analysis | 24.9% | – |
| CyberGym end-to-end (AA) | Artificial Analysis | 76.3% | – |
| Maths | |||
| FrontierMath Tiers 1–3 | Epoch AI Benchmarking Hub | 71.2% | – |
| FrontierMath Tier 4 | Epoch AI Benchmarking Hub | 46.3% | – |
| OTIS Mock AIME | Epoch AI Benchmarking Hub | 99.4% | – |
| Knowledge | |||
| SimpleQA Verified | Epoch AI Benchmarking Hub | 42.5% | – |
| LiveBench 2.0 Data Analysis | LiveBench | 79.5%† | 82.3%† |
| LiveBench Research | LiveBench | 79.5%† | 73.9%† |
| AA-Omniscience | Artificial Analysis | 4.7 | 29.8 |
| Excel Modeling Benchmark | Vals AI | 61.8% | – |
| Excel Modeling Benchmark v1.1 | Vals AI | 69.2% | – |
| AA-Omniscience: accuracy | Artificial Analysis | 42.9% | 49.1% |
| AA-Omniscience: non-hallucination | Artificial Analysis | 33.3% | 62.0% |
| Instruction following | |||
| MultiChallenge | Scale AI SEAL | 74.9%† | – |
| Multimodal | |||
| LiveBench Design | LiveBench | 83.4%† | 82.3%† |
| MMMU-Pro | Artificial Analysis | 80.6% | 84.7% |
| Long context | |||
| AA-LCR | Artificial Analysis | 82.7% | 82.7% |
| MLCR | Artificial Analysis | 26.7% | – |
| Composite | |||
| LiveBench 2.0 | LiveBench | 74.0%† | 77.2%† |
| AA Intelligence Index v4.3.2 | Artificial Analysis | 45.1 | 48.1 |
| Vals Index | Vals AI | 61.3 | – |
| Vals Finance Index | Vals AI | 60.9% | – |
| Vals Router Index | Vals AI | 61.3%† | – |
| AA Cyber Index | Artificial Analysis | 56.8 | – |
| Safety & honesty | |||
| FORTRESS | Scale AI SEAL | – | 18.2%† |
| Metadata | |||
| AA Openness Index | Artificial Analysis | 44.4 | – |
Data as of 2026-10-27. Best configuration of each model; every score links to its source on the model pages.
What changed
- DeepSeek V4.1 Flash 1017 (max): 10 new results (MLCR, AA Cyber Index, CWE-Bench (AA)…).
- DeepSeek V4.1 Flash 1017 (max): BenchLeader Index 63.1 → 63.9.
- DeepSeek V4.1 Flash 1017 (max): 11 new results (GPQA Diamond, FrontierMath Tiers 1–3, FrontierMath Tier 4…).
- DeepSeek V4.1 Flash 1017 (max): 1 new result (FrontierCode 1.1 (AA)); 2 score revisions.
Related comparisons
- DeepSeek V4.1 Flash 1017 vs Claude Opus 5.5
- DeepSeek V4.1 Flash 1017 vs Claude Fable 5.1
- DeepSeek V4.1 Flash 1017 vs GPT-6 Astra
- GPT-6.1 Terra vs Claude Opus 5.5
- GPT-6.1 Terra vs Claude Fable 5.1
- GPT-6.1 Terra vs GPT-6 Astra
- GPT-6.1 Terra vs Muse Wonder Full
- DeepSeek V4.1 Flash 1017 vs GPT-6.1 Sol
Rankings: Best LLM overall · Best LLM for agents · Best cheap LLMs
DeepSeek V4.1 Flash 1017 vs GPT-6.1 Terra: questions
- Is DeepSeek V4.1 Flash 1017 better than GPT-6.1 Terra?
- DeepSeek V4.1 Flash 1017 (max) and GPT-6.1 Terra (max) are neck and neck on quality (63.9 vs 65.9, within the ±8.0 margin). The BenchLeader Index combines every independent quality benchmark; the two are within the margin of error as of 2026-10-27, so check the category scores for your use.
- Which is cheaper, DeepSeek V4.1 Flash 1017 or GPT-6.1 Terra, and will that change?
- DeepSeek V4.1 Flash 1017 is cheaper today: $0.525 against $2.00 per million tokens, blended at three input tokens per output token. No price change has been announced for either.
- Which is better for long documents?
- They are level on long-context benchmarks (66 vs 66). Both take about 1.1M tokens: DeepSeek V4.1 Flash 1017 1,048,576, GPT-6.1 Terra 1,050,000.
- Which has lower latency?
- DeepSeek V4.1 Flash 1017 (max) answers first: 13.0 s against 131.8 s to the first answer token, as Artificial Analysis measures it. Reasoning effort drives most of this: a higher setting thinks longer before it answers.
- Which is faster, DeepSeek V4.1 Flash 1017 or GPT-6.1 Terra?
- DeepSeek V4.1 Flash 1017 streams faster: 169 against 91 output tokens per second.
- Do independent benchmarks agree on DeepSeek V4.1 Flash 1017 vs GPT-6.1 Terra?
- Across the 14 benchmarks both have, DeepSeek V4.1 Flash 1017 leads 4, GPT-6.1 Terra leads 9 and 1 is a tie. Every overall index that rates both puts GPT-6.1 Terra ahead.
- When was GPT-6.1 Terra released?
- GPT-6.1 Terra was released on 26 Oct 2026.