Trinity Large Thinking vs Qwen3.5-Flash
Qwen3.5-Flash costs 3.1x less per output token than Trinity Large Thinking ($0.26 vs $0.80 per million). Updated October 22, 2026; 33 benchmarks have a result for both.
Trinity Large Thinking or Qwen3.5-Flash: which to pick
Only Qwen3.5-Flash has a coding score, 41.1 on the Vibe Coding Index; Trinity Large Thinking has none, so the two cannot be ranked on coding.
Pick Trinity Large Thinking for work measured by LMArena WebDev: React or CaseLaw (Vals AI), where it leads.
- Shared results
- 33
- Trinity Large Thinking only
- 19
- Qwen3.5-Flash only
- 22
- Like-for-like categories
- 5 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
No call
Only Qwen3.5-Flash has a coding score, so no call is made.
One side not measured
Intelligence
AA Intelligence Index
No call
Only Trinity Large Thinking has a AA Intelligence Index result, 10.8.
Directional only
Agentic work
AA Agentic Index
No call
Only Trinity Large Thinking has a AA Agentic Index result, 1.0.
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 Trinity Large Thinking has a Design Arena: websites result, 1137.
Directional only
Long documents
Listed context window
Qwen3.5-Flash
Qwen3.5-Flash takes 1M tokens against 262K.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
Qwen3.5-Flash
Trinity Large Thinking $0.0006, 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
- Trinity Large Thinking not benchmarked, Qwen3.5-Flash 41.1 from 2 benchmarks.
- Widest category
- General, 9.1 points apart on average over 17 shared benchmarks.
- Widest single gap
- LMArena Text: Chinese: Trinity Large Thinking 1371 against Qwen3.5-Flash 1445, 12.4 points apart on a 0 to 100 scale.
- Output price
- Trinity Large Thinking $0.80 against Qwen3.5-Flash $0.26 per million output tokens, 3.1x apart.
- Context window
- Trinity Large Thinking takes 262K 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 | Trinity Large Thinking | Bars | Qwen3.5-Flash | Ties |
|---|---|---|---|---|
| Coding | 1 | 4 | ||
| Reasoning | 0 | 3 | ||
| Math | 0 | 2 | ||
| Knowledge | 1 | 5 | ||
| General | 0 | 17 | ||
| All shared boards | 2 | 31 |
CodingLike-for-likeSix of these boards feed the Vibe Coding Index14
| Benchmark | Trinity Large Thinking | Qwen3.5-Flash | Leader |
|---|---|---|---|
| LMArena WebDev | 1238 | 1244 | Qwen3.5-Flash |
| AA Coding Index | 25.8 | Not measured | Only Trinity Large Thinking measured |
| 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 |
| SciCode | 40.6% | Not measured | Only Trinity Large Thinking measured |
| Terminal-Bench Hard (Artificial Analysis run) | 22.7% | Not measured | Only Trinity Large Thinking measured |
| ALE-Bench | Not measured | 265.9 | Only Qwen3.5-Flash measured |
| LMArena Text: coding | 1357 | 1412 | Qwen3.5-Flash |
| LMArena Text: software and IT | 1362 | 1416 | Qwen3.5-Flash |
| LMArena WebDev: React | 1231 | 1229 | Trinity Large Thinking |
| LMArena WebDev: HTML | 1240 | 1291 | Qwen3.5-Flash |
| Neither model has a result on the other 28 boards here | |||
IntelligenceDirectional onlyPublic boards, shown as published, kept out of the index2 boards, none shared
| Benchmark | Trinity Large Thinking | Qwen3.5-Flash | Leader |
|---|---|---|---|
| AA Intelligence Index | 10.8 | Not measured | Only Trinity Large Thinking measured |
| Epoch Capabilities Index | Not measured | 143.5 | Only Qwen3.5-Flash measured |
Agentic workDirectional onlyPublic boards, shown as published, kept out of the index2 boards, none shared
| Benchmark | Trinity Large Thinking | Qwen3.5-Flash | Leader |
|---|---|---|---|
| AA Agentic Index | 1.0 | Not measured | Only Trinity Large Thinking measured |
| τ²-Bench Telecom | 90.1% | Not measured | Only Trinity Large Thinking measured |
| Neither model has a result on the other 39 boards here | |||
ReasoningLike-for-likePublic boards, shown as published, kept out of the index03
| Benchmark | Trinity Large Thinking | Qwen3.5-Flash | Leader |
|---|---|---|---|
| GPQA Diamond | Not measured | 82.8% | Only Qwen3.5-Flash measured |
| MMMU Pro (Vals AI) | Not measured | 81.9% | Only Qwen3.5-Flash measured |
| GPQA Diamond (Artificial Analysis run) | 75.2% | Not measured | Only Trinity Large Thinking measured |
| Humanity's Last Exam (Artificial Analysis run) | 15.8% | Not measured | Only Trinity Large Thinking measured |
| GPQA Diamond (Epoch AI run) | Not measured | 82.3% | Only Qwen3.5-Flash measured |
| Chess puzzles | Not measured | 14.0% | Only Qwen3.5-Flash measured |
| Mystery game puzzles | Not measured | 20.0% | Only Qwen3.5-Flash measured |
| LMArena Text: hard prompts | 1342 | 1403 | Qwen3.5-Flash |
| LMArena Text: expert | 1359 | 1407 | Qwen3.5-Flash |
| LMArena Text: hard prompts in English | 1356 | 1409 | Qwen3.5-Flash |
| Neither model has a result on the other 12 boards here | |||
MathLike-for-likePublic boards, shown as published, kept out of the index02
| Benchmark | Trinity Large Thinking | Qwen3.5-Flash | Leader |
|---|---|---|---|
| AIME | Not measured | 92.5% | Only Qwen3.5-Flash measured |
| OTIS Mock AIME | Not measured | 84.4% | Only Qwen3.5-Flash measured |
| FrontierMath, tiers 1 to 3 | Not measured | 18.2% | Only Qwen3.5-Flash measured |
| LMArena Text: math | 1365 | 1407 | Qwen3.5-Flash |
| LMArena Text: mathematical work | 1350 | 1413 | Qwen3.5-Flash |
| Neither model has a result on the other 8 boards here | |||
KnowledgeLike-for-likePublic boards, shown as published, kept out of the index15
| Benchmark | Trinity Large Thinking | 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) | 57.9% | 56.0% | Trinity Large Thinking |
| CorpFin (Vals AI) | 54.7% | 63.6% | Qwen3.5-Flash |
| 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 | Not measured | 20.3% | Only Qwen3.5-Flash measured |
| LMArena Text: science | 1355 | 1415 | Qwen3.5-Flash |
| LMArena Text: medicine and healthcare | 1349 | 1407 | Qwen3.5-Flash |
| LMArena Text: legal and government | 1345 | 1398 | Qwen3.5-Flash |
| LMArena Text: business and finance | 1329 | 1394 | Qwen3.5-Flash |
| Neither model has a result on the other 6 boards here | |||
Web designDirectional onlyPublic boards, shown as published, kept out of the index8 boards, none shared
| Benchmark | Trinity Large Thinking | Qwen3.5-Flash | Leader |
|---|---|---|---|
| Design Arena: websites | 1137 | Not measured | Only Trinity Large Thinking measured |
| Design Arena: UI components | 1042 | Not measured | Only Trinity Large Thinking measured |
| Design Arena: game dev | 1078 | Not measured | Only Trinity Large Thinking measured |
| Design Arena: data viz | 1098 | Not measured | Only Trinity Large Thinking measured |
| Design Arena: 3D | 1081 | Not measured | Only Trinity Large Thinking measured |
| Design Arena: all code categories | 1116 | Not measured | Only Trinity Large Thinking measured |
| Design Arena: SVG | 1016 | Not measured | Only Trinity Large Thinking measured |
| Design Arena: ASCII art | 1041 | Not measured | Only Trinity Large Thinking measured |
| Neither model has a result on the other 1 board here | |||
GeneralLike-for-likePublic boards, shown as published, kept out of the index017
| Benchmark | Trinity Large Thinking | Qwen3.5-Flash | Leader |
|---|---|---|---|
| SAGE (Vals AI) | Not measured | 42.5% | Only Qwen3.5-Flash measured |
| MedScribe (Vals AI) | Not measured | 70.6% | Only Qwen3.5-Flash measured |
| IFBench | 56.3% | Not measured | Only Trinity Large Thinking measured |
| Long context reasoning (AA-LCR) | 38.0% | Not measured | Only Trinity Large Thinking measured |
| DTBench | Not measured | 82.9% | Only Qwen3.5-Flash measured |
| LMCA | Not measured | 29.1% | Only Qwen3.5-Flash measured |
| Surface Evolver Bench | 15.6% | Not measured | Only Trinity Large Thinking measured |
| LMArena Text | 1340 | 1397 | Qwen3.5-Flash |
| LMArena Text: instruction following | 1326 | 1374 | Qwen3.5-Flash |
| LMArena Text: creative writing | 1308 | 1343 | Qwen3.5-Flash |
| LMArena Text: English | 1356 | 1404 | Qwen3.5-Flash |
| LMArena Text: non-English | 1325 | 1385 | Qwen3.5-Flash |
| LMArena Text: Chinese | 1371 | 1445 | Qwen3.5-Flash |
| LMArena Text: German | 1356 | 1390 | Qwen3.5-Flash |
| LMArena Text: Japanese | 1312 | 1369 | Qwen3.5-Flash |
| LMArena Text: Korean | 1306 | 1344 | Qwen3.5-Flash |
| LMArena Text: French | 1366 | 1413 | Qwen3.5-Flash |
| LMArena Text: Spanish | 1327 | 1400 | Qwen3.5-Flash |
| LMArena Text: Russian | 1338 | 1380 | Qwen3.5-Flash |
| LMArena Text: Polish | 1312 | 1376 | Qwen3.5-Flash |
| LMArena Text: multi-turn | 1320 | 1393 | Qwen3.5-Flash |
| LMArena Text: longer queries | 1318 | 1392 | Qwen3.5-Flash |
| LMArena Text: writing and language | 1317 | 1369 | Qwen3.5-Flash |
| LMArena Text: entertainment and media | 1296 | 1347 | Qwen3.5-Flash |
| 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 | Trinity Large Thinking | 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 | Arcee AI Trinity Large Thinking | Qwen3.5-Flash |
|---|---|---|
| Maker | Arcee AI | Qwen |
| Weights | Open weights | No public weights listed |
| Released | April 1, 2026 | February 25, 2026 |
| Context window | 262K | 1M |
| Input $/Mtok | $0.25 | $0.065 |
| Output $/Mtok | $0.80 | $0.26 |
| Coding index | Not benchmarked | 41.1 |
| Independent measurements behind it | 1 | 2 |
| Cost per solved task | Not computable | $0.40 |
| Source | OpenRouter listing | OpenRouter listing |
Questions
Is Trinity Large Thinking better than Qwen3.5-Flash for coding?
Only Qwen3.5-Flash has a coding score, 41.1 on the Vibe Coding Index; Trinity Large Thinking has none, so the two cannot be ranked on coding. Pick Trinity Large Thinking for work measured by LMArena WebDev: React or CaseLaw (Vals AI), where it leads.
Which is cheaper, Trinity Large Thinking or Qwen3.5-Flash?
Trinity Large Thinking costs $0.25 per million input tokens and $0.80 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, Trinity Large Thinking or Qwen3.5-Flash?
Qwen3.5-Flash, with 1M tokens against 262K.
How many benchmarks is the Trinity Large Thinking vs Qwen3.5-Flash comparison based on?
33 benchmarks have a result for both models as of October 22, 2026. Trinity Large Thinking's coding index rests on 1 independent benchmark and Qwen3.5-Flash's on 2. Where only one model has a result, the page says so instead of naming a winner.