GPT-6.1 Terra vs Qwen3.5-Flash
GPT-6.1 Terra leads Qwen3.5-Flash on coding, 77.4 to 41.1 on the Vibe Coding Index, and Qwen3.5-Flash costs 19.2x less per output token ($0.26 vs $5.00 per million). Updated October 29, 2026; 9 benchmarks have a result for both.
GPT-6.1 Terra or Qwen3.5-Flash: which to pick
Pick GPT-6.1 Terra for coding: it scores 77.4 on the Vibe Coding Index against 41.1 for Qwen3.5-Flash.
Pick Qwen3.5-Flash for output tokens 19.2x cheaper ($0.26 vs $5.00 per million).
- Shared results
- 9
- GPT-6.1 Terra only
- 27
- Qwen3.5-Flash only
- 46
- Like-for-like categories
- 4 of 9
Only benchmarks that tested both models count toward a pick; a result only one model has is shown but not compared.
Which one for your work
Each card is decided by one number both models carry. Where only one has it, the card says so and makes no call.
Coding
Vibe Coding Index, six public coding benchmarks
GPT-6.1 Terra
GPT-6.1 Terra scores 77.4 against 41.1.
4 and 2 benchmarks behind the scores
Intelligence
AA Intelligence Index
No call
Only GPT-6.1 Terra has a AA Intelligence Index result, 51.0.
Directional only
Reasoning
GPQA Diamond
No call
Only Qwen3.5-Flash has a GPQA Diamond result, 82.8%.
Directional only
Math
AIME
No call
Only Qwen3.5-Flash has a AIME result, 92.5%.
Directional only
Knowledge
MMLU Pro
No call
Only Qwen3.5-Flash has a MMLU Pro result, 84.1%.
Directional only
Web design
Design Arena: websites
No call
Only GPT-6.1 Terra has a Design Arena: websites result, 1283.
Directional only
General
Vals Index
No call
Only GPT-6.1 Terra has a Vals Index result, 60.5%.
Directional only
Long documents
Listed context window
GPT-6.1 Terra
GPT-6.1 Terra takes 1.05M tokens against 1M.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
Qwen3.5-Flash
GPT-6.1 Terra $0.0035, Qwen3.5-Flash $0.0002 per turn.
List prices
Cost per solved task
One SWE-bench Verified task, solved
No call
Only Qwen3.5-Flash has the number, $0.40.
Directional only
At a glance
- Coding index
- GPT-6.1 Terra 77.4 from 4 benchmarks, Qwen3.5-Flash 41.1 from 2 benchmarks.
- Widest category
- Coding, 63.2 points apart on average over 4 shared benchmarks.
- Widest single gap
- ALE-Bench: GPT-6.1 Terra 2608.8 against Qwen3.5-Flash 265.9, 78.1 points apart on a 0 to 100 scale.
- Output price
- GPT-6.1 Terra $5.00 against Qwen3.5-Flash $0.26 per million output tokens, 19.2x apart.
- Context window
- GPT-6.1 Terra takes 1.05M tokens, Qwen3.5-Flash 1M.
Benchmark by benchmark
First the score: boards each model leads, per category, where both have a result. Then every board, a category to a fold; bars are each result on a 0 to 100 scale, so compare the two in a row, not one row with another.
| Category | GPT-6.1 Terra | Bars | Qwen3.5-Flash | Ties |
|---|---|---|---|---|
| Coding | 4 | 0 | ||
| Reasoning | 2 | 0 | ||
| Math | 2 | 0 | ||
| Knowledge | 1 | 0 | ||
| All shared boards | 9 | 0 |
CodingLike-for-likeSix of these boards feed the Vibe Coding Index40
| Benchmark | GPT-6.1 Terra | Qwen3.5-Flash | Leader |
|---|---|---|---|
| LiveBench coding | 94.3% | Not measured | Only GPT-6.1 Terra measured |
| LMArena WebDev | 1673 | 1244 | GPT-6.1 Terra |
| LiveCodeBench (Vals AI) | Not measured | 83.3% | Only Qwen3.5-Flash measured |
| SWE-bench Verified (Vals AI) | Not measured | 64.4% | Only Qwen3.5-Flash measured |
| Terminal-Bench 4 (Vals AI) | 49.5% | Not measured | Only GPT-6.1 Terra measured |
| Vibe Code Bench (Vals AI) | 84.0% | Not measured | Only GPT-6.1 Terra measured |
| SciCode | 55.1% | Not measured | Only GPT-6.1 Terra measured |
| ALE-Bench | 2608.8 | 265.9 | GPT-6.1 Terra |
| LMArena Text: coding | Not measured | 1412 | Only Qwen3.5-Flash measured |
| LMArena Text: software and IT | Not measured | 1416 | Only Qwen3.5-Flash measured |
| LMArena WebDev: React | 1657 | 1229 | GPT-6.1 Terra |
| LMArena WebDev: HTML | 1719 | 1291 | GPT-6.1 Terra |
| Neither model has a result on the other 31 boards here | |||
IntelligenceDirectional onlyPublic boards, shown as published, kept out of the index2 boards, none shared
| Benchmark | GPT-6.1 Terra | Qwen3.5-Flash | Leader |
|---|---|---|---|
| AA Intelligence Index | 51.0 | Not measured | Only GPT-6.1 Terra measured |
| Epoch Capabilities Index | Not measured | 143.5 | Only Qwen3.5-Flash measured |
Agentic workDirectional onlyPublic boards, shown as published, kept out of the index7 boards, none shared
| Benchmark | GPT-6.1 Terra | Qwen3.5-Flash | Leader |
|---|---|---|---|
| LiveBench research | 73.9% | Not measured | Only GPT-6.1 Terra measured |
| LiveBench browser | 93.1% | Not measured | Only GPT-6.1 Terra measured |
| LiveBench long agentic | 48.6% | Not measured | Only GPT-6.1 Terra measured |
| Tax Agent Bench (Vals AI) | 15.2% | Not measured | Only GPT-6.1 Terra measured |
| Legal Research Bench (Vals AI) | 26.0% | Not measured | Only GPT-6.1 Terra measured |
| Harvey's Legal Agent Benchmark (Vals AI) | 1.7% | Not measured | Only GPT-6.1 Terra measured |
| APEX-Agents | 68.3% | Not measured | Only GPT-6.1 Terra measured |
| Neither model has a result on the other 35 boards here | |||
ReasoningLike-for-likePublic boards, shown as published, kept out of the index20
| Benchmark | GPT-6.1 Terra | Qwen3.5-Flash | Leader |
|---|---|---|---|
| GPQA Diamond | Not measured | 82.8% | Only Qwen3.5-Flash measured |
| LiveBench reasoning | 88.7% | Not measured | Only GPT-6.1 Terra measured |
| MMMU Pro (Vals AI) | Not measured | 81.9% | Only Qwen3.5-Flash measured |
| Humanity's Last Exam (Artificial Analysis run) | 46.2% | Not measured | Only GPT-6.1 Terra measured |
| GPQA Diamond (Epoch AI run) | 94.3% | 82.3% | GPT-6.1 Terra |
| Chess puzzles | Not measured | 14.0% | Only Qwen3.5-Flash measured |
| Mystery game puzzles | 74.0% | 20.0% | GPT-6.1 Terra |
| EBR-bench | 78.1% | Not measured | Only GPT-6.1 Terra measured |
| LMArena Text: hard prompts | Not measured | 1403 | Only Qwen3.5-Flash measured |
| LMArena Text: expert | Not measured | 1407 | Only Qwen3.5-Flash measured |
| LMArena Text: hard prompts in English | Not measured | 1409 | Only Qwen3.5-Flash measured |
| Humanity's Last Exam (Scale run) | 35.7% | Not measured | Only GPT-6.1 Terra measured |
| Neither model has a result on the other 11 boards here | |||
MathLike-for-likePublic boards, shown as published, kept out of the index20
| Benchmark | GPT-6.1 Terra | Qwen3.5-Flash | Leader |
|---|---|---|---|
| AIME | Not measured | 92.5% | Only Qwen3.5-Flash measured |
| OTIS Mock AIME | 100.0% | 84.4% | GPT-6.1 Terra |
| FrontierMath, tiers 1 to 3 | 90.9% | 18.2% | GPT-6.1 Terra |
| FrontierMath, tier 4 | 92.7% | Not measured | Only GPT-6.1 Terra measured |
| LMArena Text: math | Not measured | 1408 | Only Qwen3.5-Flash measured |
| LMArena Text: mathematical work | Not measured | 1413 | Only Qwen3.5-Flash measured |
| Neither model has a result on the other 8 boards here | |||
KnowledgeLike-for-likePublic boards, shown as published, kept out of the index10
| Benchmark | GPT-6.1 Terra | Qwen3.5-Flash | Leader |
|---|---|---|---|
| MMLU Pro | Not measured | 84.1% | Only Qwen3.5-Flash measured |
| LegalBench (Vals AI) | Not measured | 84.3% | Only Qwen3.5-Flash measured |
| CaseLaw (Vals AI) | Not measured | 56.0% | Only Qwen3.5-Flash measured |
| CorpFin (Vals AI) | Not measured | 63.6% | Only Qwen3.5-Flash measured |
| TaxEval (Vals AI) | Not measured | 72.2% | Only Qwen3.5-Flash measured |
| MortgageTax (Vals AI) | Not measured | 67.4% | Only Qwen3.5-Flash measured |
| MedCode (Vals AI) | Not measured | 33.0% | Only Qwen3.5-Flash measured |
| SimpleQA Verified | 57.3% | 20.3% | GPT-6.1 Terra |
| LMArena Text: science | Not measured | 1415 | Only Qwen3.5-Flash measured |
| LMArena Text: medicine and healthcare | Not measured | 1407 | Only Qwen3.5-Flash measured |
| LMArena Text: legal and government | Not measured | 1397 | Only Qwen3.5-Flash measured |
| LMArena Text: business and finance | Not measured | 1394 | Only Qwen3.5-Flash measured |
| Neither model has a result on the other 8 boards here | |||
Web designDirectional onlyPublic boards, shown as published, kept out of the index5 boards, none shared
| Benchmark | GPT-6.1 Terra | Qwen3.5-Flash | Leader |
|---|---|---|---|
| Design Arena: websites | 1283 | Not measured | Only GPT-6.1 Terra measured |
| Design Arena: UI components | 1277 | Not measured | Only GPT-6.1 Terra measured |
| Design Arena: game dev | 1348 | Not measured | Only GPT-6.1 Terra measured |
| Design Arena: all code categories | 1295 | Not measured | Only GPT-6.1 Terra measured |
| LiveBench design | 82.3% | Not measured | Only GPT-6.1 Terra measured |
| Neither model has a result on the other 4 boards here | |||
GeneralDirectional onlyPublic boards, shown as published, kept out of the index26 boards, none shared
| Benchmark | GPT-6.1 Terra | Qwen3.5-Flash | Leader |
|---|---|---|---|
| Vals Index | 60.5% | Not measured | Only GPT-6.1 Terra measured |
| LiveBench data analysis | 82.3% | Not measured | Only GPT-6.1 Terra measured |
| SAGE (Vals AI) | Not measured | 84.9% | Only Qwen3.5-Flash measured |
| MedScribe (Vals AI) | Not measured | 70.6% | Only Qwen3.5-Flash measured |
| Long context reasoning (AA-LCR) | 82.7% | Not measured | Only GPT-6.1 Terra measured |
| DTBench | Not measured | 82.9% | Only Qwen3.5-Flash measured |
| LMCA | Not measured | 29.1% | Only Qwen3.5-Flash measured |
| Furniture assembly | 79.2% | Not measured | Only GPT-6.1 Terra measured |
| LMArena Text | Not measured | 1397 | Only Qwen3.5-Flash measured |
| LMArena Text: instruction following | Not measured | 1375 | Only Qwen3.5-Flash measured |
| LMArena Text: creative writing | Not measured | 1343 | Only Qwen3.5-Flash measured |
| LMArena Text: English | Not measured | 1404 | Only Qwen3.5-Flash measured |
| LMArena Text: non-English | Not measured | 1385 | Only Qwen3.5-Flash measured |
| LMArena Text: Chinese | Not measured | 1446 | Only Qwen3.5-Flash measured |
| LMArena Text: German | Not measured | 1391 | Only Qwen3.5-Flash measured |
| LMArena Text: Japanese | Not measured | 1368 | Only Qwen3.5-Flash measured |
| LMArena Text: Korean | Not measured | 1343 | Only Qwen3.5-Flash measured |
| LMArena Text: French | Not measured | 1414 | Only Qwen3.5-Flash measured |
| LMArena Text: Spanish | Not measured | 1399 | Only Qwen3.5-Flash measured |
| LMArena Text: Russian | Not measured | 1380 | Only Qwen3.5-Flash measured |
| LMArena Text: Polish | Not measured | 1376 | Only Qwen3.5-Flash measured |
| LMArena Text: multi-turn | Not measured | 1393 | Only Qwen3.5-Flash measured |
| LMArena Text: longer queries | Not measured | 1392 | Only Qwen3.5-Flash measured |
| LMArena Text: writing and language | Not measured | 1369 | Only Qwen3.5-Flash measured |
| LMArena Text: entertainment and media | Not measured | 1347 | Only Qwen3.5-Flash measured |
| MultiChallenge | 74.2% | Not measured | Only GPT-6.1 Terra measured |
| Neither model has a result on the other 22 boards here | |||
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | GPT-6.1 Terra | Qwen3.5-Flash | Leader |
|---|---|---|---|
| Neither model has a result on 5 boards here | |||
What each workload costs
Three fixed token mixes at each model's listed price, so per-token rates become a bill.
Chat turn
1K input + 500 output tokens
Qwen3.5-Flash costs less
Repository review
50K input + 3K output tokens
Qwen3.5-Flash costs less
Agent loop
200K input + 10K output tokens
Qwen3.5-Flash costs less
Listed OpenRouter rates, without cache or batch discounts. A workload that does not fit a model's context window would need splitting.
Specification differences
The leading value in a row is marked; a row with a missing value names no leader. Our two rows are marked in red.
| Specification | OpenAI GPT-6.1 Terra | Qwen3.5-Flash |
|---|---|---|
| Maker | OpenAI | Qwen |
| Weights | No public weights listed | No public weights listed |
| Released | October 26, 2026 | February 25, 2026 |
| Context window | 1.05M | 1M |
| Input $/Mtok | $1.00 | $0.065 |
| Output $/Mtok | $5.00 | $0.26 |
| Coding index | 77.4 | 41.1 |
| Independent measurements behind it | 4 | 2 |
| Cost per solved task | Not computable | $0.40 |
| Source | OpenRouter listing | OpenRouter listing |
Questions
Is GPT-6.1 Terra better than Qwen3.5-Flash for coding?
Pick GPT-6.1 Terra for coding: it scores 77.4 on the Vibe Coding Index against 41.1 for Qwen3.5-Flash. Pick Qwen3.5-Flash for output tokens 19.2x cheaper ($0.26 vs $5.00 per million).
Which is cheaper, GPT-6.1 Terra or Qwen3.5-Flash?
GPT-6.1 Terra costs $1.00 per million input tokens and $5.00 per million output tokens; Qwen3.5-Flash costs $0.065 and $0.26. Cost per solved task is known for Qwen3.5-Flash only.
Which has the bigger context window, GPT-6.1 Terra or Qwen3.5-Flash?
GPT-6.1 Terra, with 1.05M tokens against 1M.
How many benchmarks is the GPT-6.1 Terra vs Qwen3.5-Flash comparison based on?
9 benchmarks have a result for both models as of October 29, 2026. GPT-6.1 Terra's coding index rests on 4 independent benchmarks and Qwen3.5-Flash's on 2. Where only one model has a result, the page says so instead of naming a winner.