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BenchLM

Data as of September 28, 2026 · How the score is built

EstablishedOpen WeightReasoning

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

Voice benchmark evidence

EVA-Bench is an owner-defined voice evaluation. Its result stays separate from BenchLM's weighted text-model ranking.

Coverage
Evaluated system
Owner label
Qwen3.5-27B
Result source
Official paper
Participant or pipeline component identified from the benchmark paper; no weighted BenchLM score is assigned.

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 changes

Category 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 scores, ranks, weighting, benchmark coverage, and evidence status
CategoryScore
AgenticRank #69 of 111Percentile 38thWeight 22%4 benchmarksVerified
32.2
CodingRank #80 of 136Percentile 41stWeight 20%2 benchmarksVerified
32.3
ReasoningRank Not rankedWeight 17%1 benchmarkVerified
52.7
MultimodalRank Not rankedWeight 12%4 benchmarksMixed sources
71.6
KnowledgeRank #82 of 160Percentile 49thWeight 12%3 benchmarksVerified
42.3
MultilingualRank #9 of 12Percentile 27thWeight 7%1 benchmarkVerified
36.8
Inst. FollowingRank #13 of 124Percentile 90thWeight 5%1 benchmarkVerified
91.5
MathWeight 5%0 benchmarksNot measured
Not measured

16 of 486 tracked benchmark slots have displayable evidence · bars run 0–100

Coverage details

How 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.

  1. Agentic4/4 verified
  2. Coding2/2 verified
  3. Reasoning1/1 verified
  4. Multimodal2/4 verified
  5. Knowledge3/3 verified
  6. Multilingual1/1 verified
  7. Inst. Following1/1 verified
  8. MathNot measured
Verified sourceProvisionalNot 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

  1. Agentic#69/111
  2. Coding#80/136
  3. ReasoningNot ranked
  4. MultimodalNot ranked
  5. Knowledge#82/160
  6. Multilingual#9/12
  7. Inst. Following#13/124
  8. MathNot ranked
Top decileTop quartileMid-fieldNot eligible

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
Coding benchmark values, best verified comparison, weight, and source status
SWE-RebenchScore58.9%Versus best verified row

Best verified: Claude Opus 4.6 · 65.3%

Gap6.4 behindWeight5% ref. weight
SWE-bench VerifiedSoftware Engineering Benchmark VerifiedScore72.4%Versus best verified row

Best verified: Claude Opus 5 · 96%

Gap23.6 behindWeightDisplay only
Agentic4 rows
Agentic benchmark values, best verified comparison, weight, and source status
BrowseCompScore61%Versus best verified row

Best verified: Atria Dawn Preview · 92.5%

Gap31.5 behindWeight8% ref. weight
Terminal-Bench 2.0Score41.6%Versus best verified row

Best verified: GPT-5.5 · 82%

Gap40.4 behindWeight6% ref. weight
OSWorld-VerifiedScore56.2%Versus best verified row

Best verified: Qwen3.8 Max · 86.1%

Gap29.9 behindWeight6% ref. weight
Gert LabsGert Labs Composite Game BenchmarkScore39.41%Versus best verified row

Best verified: Claude Opus 4.8 · 72.97%

Gap33.6 behindWeightDisplay only
Benchmark exact
Reasoning1 row
Reasoning benchmark values, best verified comparison, weight, and source status
LongBench v2Score60.6%Versus best verified row

Best verified: Qwen3.8 Max · 66.3%

Gap5.7 behindWeightWeighted 25%
Multimodal4 rows
Multimodal benchmark values, best verified comparison, weight, and source status
MMMUMassive Multi-discipline Multimodal UnderstandingScore82.3%Versus best verified row

Best verified: Qwen3.6-27B · 82.9%

Gap0.6 behindWeightDisplay only
MMVUMultimodal Multi-disciplinary Video UnderstandingScore73.3%Versus best verified row

Best verified: Qwen3.8 Max · 82.4%

Gap9.1 behindWeightDisplay only
MathVisionScore86.0%Versus best verified row

Best verified: Qwen3.8 Max · 95.2%

Gap9.2 behindWeightDisplay only
V*Score93.7%Versus best verified row

Best verified: Kimi K2.6 · 96.9%

Gap3.2 behindWeightDisplay only
Knowledge3 rows
Knowledge benchmark values, best verified comparison, weight, and source status
MMLU-ProMassive Multitask Language Understanding ProfessionalScore86.1%Versus best verified row

Best verified: Qwen3.7 Max · 89.6%

Gap3.5 behindWeight6% ref. weight
GPQAGraduate-Level Google-Proof Q&AScore85.5%Versus best verified row

Best verified: GPT-6 Astra · 96%

Gap10.5 behindWeight3% ref. weight
SuperGPQASuperGPQA: Scaling LLM Evaluation Across 285 Graduate DisciplinesScore65.6%Versus best verified row

Best verified: Qwen 3.6 Max (preview) · 73.9%

Gap8.3 behindWeight2% ref. weight
Multilingual1 row
Multilingual benchmark values, best verified comparison, weight, and source status
MMLU-ProXScore82.2%Versus best verified row

Best verified: Qwen3.7 Max · 87%

Gap4.8 behindWeightWeighted 100%
Inst. Following1 row
Inst. Following benchmark values, best verified comparison, weight, and source status
IFEvalInstruction-Following EvalScore95%Versus best verified row

Best verified: Qwen3.5-27B · 95%

GapBest verifiedWeightDisplay only

Bars run 0–100; the dark tick marks the best source-verified value

All 16 rows

Lineage

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.

  1. Mar 4, 2026 · you are here

    Qwen3.5-27B

    Score 43.8 · Price not listed

Base entry

Radar

Qwen3.5-27B release history

Full 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 parameters

Questions

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.

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