Models / Qwen3.5-0.8B

Alibaba

Qwen3.5-0.8B

Qwen3.5-0.8B trails most of the field on what it has been measured on; it is not on sale through OpenRouter, so it is here for reference.

Compared with the models available today (it is not on sale itself), in the bottom quarter for agents and tool use, reasoning and knowledge, and writing.

Results as of 8 October 2026, from 21 results on 2 sources; prices checked 8 Oct 2026.

Price per million tokens
Not available through OpenRouter
Speed
No speed yet
Artificial Analysis lists it but has not published a speed for it yet
Intelligence Index
6.1
Artificial Analysis, at thinking reasoning; 5.4–6.1 across 2 settings
Spring Prompt overall
Not ranked yet
needs our benchmarks and two groups of results
Developer
Alibaba, based in China
Weights not published
Released
2 Mar 2026 (Artificial Analysis)

Where it stands among the models you could choose

Ranked only among models available today (those you can call through OpenRouter), in the groups people choose between: the same price band, similar intelligence, the same speed, developers based in the same place. Retired models are left out.

AmongIntelligence IndexSpring Prompt overallPrice, blendedSpeed
Models available today 187th of 199Claude Opus 5.5 – – –
Models from developers based in China 87th of 87MiMo-V2.6-Pro – – –

The name under each rank is the leader of that group; green is the top quarter of the group and red the bottom quarter, by rank. Price is shown as a percentile: the share of the group that costs less, so lower is cheaper. "Sets this group" marks the measure the group is defined by. Spring Prompt overall is our score out of 100 across every source (how it works). Price is the developer's list price (or the typical OpenRouter host where we haven't read one) for three input tokens to one output; intelligence and speed are from Artificial Analysis (data sourced from Artificial Analysis); "similar intelligence" means within 4 points on its Intelligence Index. Developer locations are where each company is based, not where a model is served. Groups under 3 models are not ranked.

How good is it, and for what?

Each use case ranks the available models its benchmarks measured. The bar shows its percentile in that field, best to the right. Open a row for the results behind it.

Product listingsTurning a sparse product feed and photos into listings that can go live Not measured
  • CatalogBench: has not measured this model.
Decks from an analysisTurning a finished analysis into a deck you could present as it is Not measured
  • DeckBench: has not measured this model.
User surveysPlanning a user survey and reading its results without being misled Not measured
  • SurveyBench: has not measured this model.
Marketing planningPlanning a year of ad spend without overspending Not measured
  • ROASBench: has not measured this model.
Agents and tool useMulti-step tasks with tools: support desks, coding agents, function calls 163rd of 165 available
BenchmarkQwen3.5-0.8BRank among availableBest available
Artificial AnalysisArtificial Analysis: Terminal-Bench 2.1 · reported 0.4% 113th of 116 Claude Fable 5.1 91.4%
  • tau2-bench: has not measured this model.
  • Berkeley Function Calling Leaderboard (BFCL) V4: pending. Its latest results are dated 16 Dec 2025, before this model was released.
  • OpenHands Index: has not measured this model.
  • Microsoft STATE-Bench: has not measured this model.
  • Vending-Bench 2: has not measured this model.
Professional workReal tasks from banking, consulting and law, business documents and freelance projects Not measured
  • APEX-Agents: has not measured this model.
  • GDP.pdf: has not measured this model.
  • Remote Labor Index: has not measured this model.
Reasoning and knowledgeHard questions across science, maths and general knowledge 184th of 199 available
BenchmarkQwen3.5-0.8BRank among availableBest available
Artificial AnalysisArtificial Analysis: Artificial Analysis Intelligence Index · reported 6.1 187th of 199 Claude Opus 5.5 57.6
Artificial AnalysisArtificial Analysis: Humanity's Last Exam · reported 5.1% 159th of 198 Claude Opus 5.5 61.4%
Artificial AnalysisArtificial Analysis: GPQA Diamond · reported 23.6% 187th of 188 GPT-6 Astra 96.3%
WritingWhat people prefer in blind comparisons, and judged writing quality 180th of 189 available
BenchmarkQwen3.5-0.8BRank among availableBest available
UGI LeaderboardUGI: writing score · reported 23.0 132nd of 139 Gemini 3.8 Flash 78.6
  • Arena (formerly LMArena): has not measured this model.
Sticking to the factsSummarising without inventing things, and factual answers Not measured
  • Vectara Hallucination Leaderboard: has not measured this model.
  • Arena (formerly LMArena): has not measured this model.
  • SimpleQA Verified (Epoch AI): has not measured this model.
Speed under pressureGood decisions against a real clock (fast chess) Not measured
  • BulletBench: has not measured this model.

Nearest alternatives

Against Qwen3.5-0.8B, Intelligence Index 6.1. "Similar intelligence" means within 4 points or better.

Against the alternatives

The models you would most likely weigh it against: the leaders of the groups above.

Scroll sideways to see every alternative.

Qwen3.5-0.8BClaude Opus 5.5
most intelligent available today
MiMo-V2.6-Pro
most intelligent from China
Qwen3.8-Max (0902)
Alibaba's best other model
GPT-6.1 Sol
near the top overall
Price per million tokens – $8.00$0.54$3.00$4.00
Tokens a second – 97393755
Intelligence Index 6.1 57.646.345.451.8
Spring Prompt overall – 85–4881
Rank among available models, by use case
Product listings – 6th–17th2nd
Decks from an analysis – 5th–11th3rd
User surveys – 2nd––1st
Marketing planning – 5th–14th–
Agents and tool use 163rd of 165 2nd15th7th3rd
Professional work – 3rd–18th9th
Reasoning and knowledge 184th of 199 1st10th17th5th
Writing 180th of 189 4th20th16th15th
Sticking to the facts – 2nd8th38th18th
Speed under pressure – ––––

Shaded figures are better than Qwen3.5-0.8B's. A dash means no result.

Where it has been measured

Every result

21 published results from 2 sources, each in the source's own units, with the configuration that produced it.

Artificial Analysis · reported by Artificial Analysis · 18 results
MeasureValueRankConfigurationDated
Artificial Analysis: Artificial Analysis Coding Indexindex score, higher is better 0.0 188 of 188 Qwen3.5-0.8B (thinking reasoning) 8 Oct 2026
Artificial Analysis: Artificial Analysis Coding Indexindex score, higher is better 1.2 187 of 188 Qwen3.5-0.8B (no reasoning) 8 Oct 2026
Artificial Analysis: Artificial Analysis Intelligence Indexindex score, higher is better 6.1 390 of 407 Qwen3.5-0.8B (thinking reasoning) 8 Oct 2026
Artificial Analysis: Artificial Analysis Intelligence Indexindex score, higher is better 5.4 401 of 407 Qwen3.5-0.8B (no reasoning) 8 Oct 2026
Artificial Analysis: GPQA Diamond% of questions, higher is better 11.1% 359 of 359 Qwen3.5-0.8B (thinking reasoning) 8 Oct 2026
Artificial Analysis: GPQA Diamond% of questions, higher is better 23.6% 356 of 359 Qwen3.5-0.8B (no reasoning) 8 Oct 2026
Artificial Analysis: Humanity's Last Exam% of questions, higher is better 1.1% 405 of 405 Qwen3.5-0.8B (thinking reasoning) 8 Oct 2026
Artificial Analysis: Humanity's Last Exam% of questions, higher is better 5.1% 328 of 405 Qwen3.5-0.8B (no reasoning) 8 Oct 2026
Artificial Analysis: IFBench% of instructions, higher is better 21.5% 287 of 288 Qwen3.5-0.8B (thinking reasoning) 8 Oct 2026
Artificial Analysis: IFBench% of instructions, higher is better 21.6% 286 of 288 Qwen3.5-0.8B (no reasoning) 8 Oct 2026
Artificial Analysis: Long-context reasoning (AA-LCR)% of questions, higher is better 9.0% 368 of 393 Qwen3.5-0.8B (thinking reasoning) 8 Oct 2026
Artificial Analysis: Long-context reasoning (AA-LCR)% of questions, higher is better 8.0% 370 of 393 Qwen3.5-0.8B (no reasoning) 8 Oct 2026
Artificial Analysis: Terminal-Bench 2.1% of tasks, higher is better 0.0% 184 of 187 Qwen3.5-0.8B (thinking reasoning) 8 Oct 2026
Artificial Analysis: Terminal-Bench 2.1% of tasks, higher is better 0.4% 181 of 187 Qwen3.5-0.8B (no reasoning) 8 Oct 2026
Artificial Analysis: Terminal-Bench Hard% of tasks, higher is better 0.0% 276 of 282 Qwen3.5-0.8B (thinking reasoning) 8 Oct 2026
Artificial Analysis: Terminal-Bench Hard% of tasks, higher is better 0.0% 276 of 282 Qwen3.5-0.8B (no reasoning) 8 Oct 2026
Artificial Analysis: τ²-bench telecom% of tasks, higher is better 47.7% 165 of 286 Qwen3.5-0.8B (thinking reasoning) 8 Oct 2026
Artificial Analysis: τ²-bench telecom% of tasks, higher is better 65.2% 141 of 286 Qwen3.5-0.8B (no reasoning) 8 Oct 2026

Not shown: Not your codebase or tools.

UGI Leaderboard · reported by UGI Leaderboard · 3 results
MeasureValueRankConfigurationDated
UGI: requested-length error% off the requested word count, lower is better 24.0% 225 of 379 Qwen3.5-0.8B (no reasoning) 14 Mar 2026
UGI: style adherencescore from 0 to 1, higher is better 0.32 306 of 379 Qwen3.5-0.8B (no reasoning) 14 Mar 2026
UGI: writing scorescore out of 100, higher is better 23.0 328 of 379 Qwen3.5-0.8B (no reasoning) 14 Mar 2026

Not shown: Not other format limits such as character counts or bullet counts.