Alibaba · Model release
Data as of September 28, 2026 · How the score is built
Released Mar 4, 2026262K contextAlibaba model documentation
Qwen3.5-27B
Decision readingQwen3.5-27B scores 43.8 out of 100 and ranks #106 of 201. This profile shows 16 source-displayable benchmark rows; its strongest eligible category is Multilingual at #9. Published weights can be self-hosted, but infrastructure cost varies and is not a comparable API token rate.
Released Mar 4, 2026 — see all recent releases
Qwen3.5-27B will be repriced, updated or retired. Get each notice with its source and date. Follow model changes
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Voice benchmark evidence
EVA-Bench is an owner-defined voice evaluation. Its result stays separate from BenchLM's weighted text-model ranking.
Decision snapshot
Each value carries a field reference instead of floating alone. Markers compare this model with the current ranked and priced catalog; they are not absolute quality thresholds.
Capability
43.8/100
field median 50.3#106 of 201 ranked models
Public
#106of 201
Verified —
Price
Self-hosted; infrastructure cost varies
input median $1No comparable first-party hosted token rate
Speed
77tok/s
field median 95 tok/sFirst token 31.59 s
Context
262Ktokens
field median 256,000Reported for this model; direct source link not stored
Strongest published evidence
Multilingual ranks #9. A well-rounded choice across a range of tasks.
Validate before choosing
16 published rows leave some tracked benchmark slots empty. No comparable first-party API token rate is published.
Source-linked · 16 displayable benchmark rows
Follow model changesCategory score record
Scores and ranks appear only where published evidence can be displayed. The table keeps the score, weight, cohort, and evidence state together.
| Category | Score | Rank | Percentile | Weight | Benchmarks | Evidence |
|---|---|---|---|---|---|---|
| AgenticRank #69 of 111Percentile 38thWeight 22%4 benchmarksVerified | 32.2 | 4 benchmarks | Verified | |||
| CodingRank #80 of 136Percentile 41stWeight 20%2 benchmarksVerified | 32.3 | 2 benchmarks | Verified | |||
| ReasoningRank Not rankedWeight 17%1 benchmarkVerified | 52.7 | 1 benchmark | Verified | |||
| MultimodalRank Not rankedWeight 12%4 benchmarksMixed sources | 71.6 | 4 benchmarks | Mixed sources | |||
| KnowledgeRank #82 of 160Percentile 49thWeight 12%3 benchmarksVerified | 42.3 | 3 benchmarks | Verified | |||
| MultilingualRank #9 of 12Percentile 27thWeight 7%1 benchmarkVerified | 36.8 | 1 benchmark | Verified | |||
| Inst. FollowingRank #13 of 124Percentile 90thWeight 5%1 benchmarkVerified | 91.5 | 1 benchmark | Verified | |||
| MathWeight 5%0 benchmarksNot measured | Not measured | 0 benchmarks | Not measured |
16 of 486 tracked benchmark slots have displayable evidence · bars run 0–100
Coverage detailsHow much of this is verified
Coverage is split by category so a strong number never hides a thin evidence base. Verified means the row is tied to a published source; provisional rows remain visible but separate.
- Agentic4/4 verified
- Coding2/2 verified
- Reasoning1/1 verified
- Multimodal2/4 verified
- Knowledge3/3 verified
- Multilingual1/1 verified
- Inst. Following1/1 verified
- MathNot measured
Capability shape
Each axis shows percentile within that category’s eligible cohort. The comparison outline is the median of the six nearest public-score peers; a collapsed vertex means the category is not rank-eligible.
Qwen3.5-27B category percentile values
- Agentic38th percentile
- Coding41st percentile
- ReasoningNot eligible
- MultimodalNot eligible
- Knowledge49th percentile
- Multilingual27th percentile
- Instruction following90th percentile
- MathNot eligible
The dashed outline is median of 6 nearest peers.
Eligible category ranks
- Agentic#69/111
- Coding#80/136
- ReasoningNot ranked
- MultimodalNot ranked
- Knowledge#82/160
- Multilingual#9/12
- Inst. Following#13/124
- MathNot ranked
Benchmark ledger
Coding opens by default. The marker compares each value with the best source-verified result in the catalog; provisional leaders do not set the reference. Expand the remaining categories for every published row.
Coding2 rows
| Benchmark | Score | Versus best verified row | Gap | Weight | Evidence |
|---|---|---|---|---|---|
| SWE-Rebench | Score58.9% | Versus best verified row Best verified: Claude Opus 4.6 · 65.3% | Gap6.4 behind | Weight5% ref. weight | Benchmark exact |
| SWE-bench VerifiedSoftware Engineering Benchmark Verified | Score72.4% | Versus best verified row Best verified: Claude Opus 5 · 96% | Gap23.6 behind | WeightDisplay only | Provider exact |
Agentic4 rows
| Benchmark | Score | Versus best verified row | Gap | Weight | Evidence |
|---|---|---|---|---|---|
| BrowseComp | Score61% | Versus best verified row Best verified: Atria Dawn Preview · 92.5% | Gap31.5 behind | Weight8% ref. weight | Provider exact |
| Terminal-Bench 2.0 | Score41.6% | Versus best verified row Best verified: GPT-5.5 · 82% | Gap40.4 behind | Weight6% ref. weight | Provider exact |
| OSWorld-Verified | Score56.2% | Versus best verified row Best verified: Qwen3.8 Max · 86.1% | Gap29.9 behind | Weight6% ref. weight | Provider exact |
| Gert LabsGert Labs Composite Game Benchmark | Score39.41% | Versus best verified row Best verified: Claude Opus 4.8 · 72.97% | Gap33.6 behind | WeightDisplay only | Benchmark exact |
Reasoning1 row
| Benchmark | Score | Versus best verified row | Gap | Weight | Evidence |
|---|---|---|---|---|---|
| LongBench v2 | Score60.6% | Versus best verified row Best verified: Qwen3.8 Max · 66.3% | Gap5.7 behind | WeightWeighted 25% | Provider exact |
Multimodal4 rows
| Benchmark | Score | Versus best verified row | Gap | Weight | Evidence |
|---|---|---|---|---|---|
| MMMUMassive Multi-discipline Multimodal Understanding | Score82.3% | Versus best verified row Best verified: Qwen3.6-27B · 82.9% | Gap0.6 behind | WeightDisplay only | Reported |
| MMVUMultimodal Multi-disciplinary Video Understanding | Score73.3% | Versus best verified row Best verified: Qwen3.8 Max · 82.4% | Gap9.1 behind | WeightDisplay only | Reported |
| MathVision | Score86.0% | Versus best verified row Best verified: Qwen3.8 Max · 95.2% | Gap9.2 behind | WeightDisplay only | Provider exact |
| V* | Score93.7% | Versus best verified row Best verified: Kimi K2.6 · 96.9% | Gap3.2 behind | WeightDisplay only | Provider exact |
Knowledge3 rows
| Benchmark | Score | Versus best verified row | Gap | Weight | Evidence |
|---|---|---|---|---|---|
| MMLU-ProMassive Multitask Language Understanding Professional | Score86.1% | Versus best verified row Best verified: Qwen3.7 Max · 89.6% | Gap3.5 behind | Weight6% ref. weight | Provider exact |
| GPQAGraduate-Level Google-Proof Q&A | Score85.5% | Versus best verified row Best verified: GPT-6 Astra · 96% | Gap10.5 behind | Weight3% ref. weight | Provider exact |
| SuperGPQASuperGPQA: Scaling LLM Evaluation Across 285 Graduate Disciplines | Score65.6% | Versus best verified row Best verified: Qwen 3.6 Max (preview) · 73.9% | Gap8.3 behind | Weight2% ref. weight | Provider exact |
Multilingual1 row
| Benchmark | Score | Versus best verified row | Gap | Weight | Evidence |
|---|---|---|---|---|---|
| MMLU-ProX | Score82.2% | Versus best verified row Best verified: Qwen3.7 Max · 87% | Gap4.8 behind | WeightWeighted 100% | Provider exact |
Inst. Following1 row
| Benchmark | Score | Versus best verified row | Gap | Weight | Evidence |
|---|---|---|---|---|---|
| IFEvalInstruction-Following Eval | Score95% | Versus best verified row Best verified: Qwen3.5-27B · 95% | GapBest verified | WeightDisplay only | Provider exact |
Bars run 0–100; the dark tick marks the best source-verified value
All 16 rowsLineage
The sequence follows explicit supersedes links. Each score is estimated for that model; a relative can inform a sparse estimate but never sets a floor, so a newer release can score below an earlier one. Scores and prices remain blank when the corresponding public row or first-party rate is unavailable.
Base entry
Qwen3.5-27B release history
Radar confirmed these at the source. Use Qwen3.5-27B in your work? Explore Radar to follow supported changes and choose your alerts.
Radar
Spec sheet
Each documented value carries its source. Missing fields stay visible as not sourced or not published, rather than disappearing from the page.
- API model ID
- Not published
- Context window
- 262K
- Maximum output
- Not sourced yet
- Knowledge cutoff
- Not sourced yet
- Input modalities
- Not sourced yet
- Output modalities
- Not sourced yet
- Parameters
- Not sourced yet
- Availability
- Not sourced yet
- Cloud regions
- Not tracked yet
- Lifecycle
- Established
- API capabilities
- Tool calling, structured outputs, and batch support are not tracked yet
- Prompt caching
- Not documented in the pricing record
- Self-host
- Open weights available; hardware estimate not sourced
- Rate limits
- Not tracked yet
How to read this profile
The visual layer above carries the decisions. These notes preserve the model, ranking, coverage, and family context behind the numbers.
Qwen3.5-27B ranks #106 of 201 on the public leaderboard with a score of 43.76/100. It does not yet have enough sourced coverage for a verified position.
Qwen3.5-27B is a open weight model with a 262K context window. It uses an explicit reasoning mode, which can improve complex problem solving while adding latency and token use.
Official exact-value benchmark snapshot from the Qwen/Qwen3.5-27B-FP8 model card. Native context is 262,144 tokens and the model card notes extensibility up to roughly 1M tokens with RoPE scaling.
16 of 486 tracked benchmark slots currently have displayable evidence. Missing categories stay blank.
Its strongest eligible category is Multilingual at #9, while its lowest eligible position is Knowledge at #82. a well-rounded choice across a range of tasks.
Last updated September 28, 2026. Runtime fields remain blank until a sourced snapshot exists.
Deployment options
Self-host and provider-specific paths stay separate from benchmark evidence so operating constraints are visible before a score becomes the whole decision.
Published weights are available, but BenchLM does not yet have a sourced parameter and VRAM profile for this exact model. Hardware cost estimates stay unavailable until that sizing record is complete.
Estimate VRAM from known parametersQuestions
How does Qwen3.5-27B perform overall in AI benchmarks?
Qwen3.5-27B ranks #106 out of 201 models on the public BenchAlign leaderboard, with a score of 43.76/100. Its evidence status is Estimated, and this profile shows 16 source-displayable benchmark rows. The label describes evidence depth, not a provider quality claim; inspect category rows before choosing a workload.
Is Qwen3.5-27B good for knowledge and understanding?
Qwen3.5-27B ranks #82 out of 160 eligible models for knowledge and understanding, with a public category score of 42.3/100. Higher-ranked alternatives are available for workloads where this category decides the choice. Check the underlying rows before treating the aggregate as a workload guarantee.
Is Qwen3.5-27B good for coding and programming?
Qwen3.5-27B ranks #80 out of 136 eligible models for coding and programming, with a public category score of 32.3/100. Higher-ranked alternatives are available for workloads where this category decides the choice. Check the underlying rows before treating the aggregate as a workload guarantee.
Is Qwen3.5-27B good for reasoning and logic?
Qwen3.5-27B has source-displayable benchmark coverage for reasoning and logic, but the public category table does not assign it a rank there. The individual rows remain available for inspection. A missing category position means the evidence threshold was not met; it does not convert the model's unmeasured work into a zero.
Is Qwen3.5-27B good for agentic tool use and computer tasks?
Qwen3.5-27B ranks #69 out of 111 eligible models for agentic tool use and computer tasks, with a public category score of 32.2/100. Higher-ranked alternatives are available for workloads where this category decides the choice. Check the underlying rows before treating the aggregate as a workload guarantee.
Is Qwen3.5-27B good for multimodal and grounded tasks?
Qwen3.5-27B has source-displayable benchmark coverage for multimodal and grounded tasks, but the public category table does not assign it a rank there. The individual rows remain available for inspection. A missing category position means the evidence threshold was not met; it does not convert the model's unmeasured work into a zero.
Is Qwen3.5-27B good for instruction following?
Qwen3.5-27B ranks #13 out of 124 eligible models for instruction following, with a public category score of 91.5/100. Higher-ranked alternatives are available for workloads where this category decides the choice. Check the underlying rows before treating the aggregate as a workload guarantee.
Is Qwen3.5-27B good for multilingual tasks?
Qwen3.5-27B ranks #9 out of 12 eligible models for multilingual tasks, with a public category score of 36.8/100. That places it in the current top ten for this category. Check the underlying rows before treating the aggregate as a workload guarantee.
Is Qwen3.5-27B open source?
Qwen3.5-27B is an open-weight model from Alibaba. Its weights can be downloaded for local or hosted deployment, subject to the published license. Open weight does not automatically mean open source: training data and training code may remain private, and commercial restrictions can still apply.
Does Qwen3.5-27B have full benchmark coverage on BenchLM?
No. Qwen3.5-27B currently has 27 source-displayable rows across 486 tracked benchmark slots. The profile exposes published, non-generated evidence and leaves missing categories blank until an exact evaluation is available. Coverage describes how much was measured; it is not a penalty added to an individual benchmark result.
What is the context window size of Qwen3.5-27B?
Qwen3.5-27B has a reported context window of 262K in the exact-model catalog record. The value stays visible, but the profile marks its source link as unavailable instead of presenting it as directly documented. Maximum output length remains separate because providers often publish a different limit.