Qwen3.5-27B is a dense, open-weight model with visual understanding. Its published results make it an interesting candidate for private document and reasoning workflows. These are reference results; the model, precision, tools, and hardware we deploy need their own evaluation.
Qwen’s published comparison. Named model versions; scores are on a 0–100 scale, higher is better.
Qwen3.5-27B75.0
Claude Sonnet 4.568.4
GPT-5 mini · 2025-08-0767.3
0 ← score on a 100-point scale → 100 · higher is better
View all reported scores
A 27B model. Serious visual understanding.
Benchmark
Qwen3.5-27B
Claude Sonnet 4.5
GPT-5 mini · 2025-08-07
MMMU-Pro
75.0
68.4
67.3
OmniDocBench 1.5
88.9
85.8
77.0
OSWorld-Verified
56.2
61.4
Not reported
Vendor-reported reference scores, not Telenext measurements. Qwen leads on these selected visual/document tests and trails Sonnet on the selected computer-use test. Quantized local deployments can behave differently. No overall ranking is implied. Source: Qwen3.5-27B official model card ↗.
MODEL SNAPSHOT / 2026-09-10
Reasoning, instructions, and code.
A second view of the 27B model, using results reported together in Qwen’s language table.
Qwen3.5-27B85.5
GPT-5 mini · 2025-08-0782.8
0 ← score on a 100-point scale → 100 · higher is better
View all reported scores
Reasoning, instructions, and code.
Benchmark
Qwen3.5-27B
GPT-5 mini · 2025-08-07
GPQA Diamond
85.5
82.8
IFEval
95.0
93.9
SWE-bench Verified
72.4
72.0
Reference checkpoints and source evaluation setups. These GPT-5 mini results are not GPT-5.6 Terra results. Different reasoning budgets, agent tools, precision, and test settings can change outcomes. Source: Qwen3.5-27B official model card ↗.
MODEL SNAPSHOT / 2026-09-10
A larger local tier, close on selected agent tasks.
DeepSeek’s 0731 release comparison. This is an enterprise-scale checkpoint, not a small desktop model.
DeepSeek V4 Flash-073182.7
Claude Opus 4.885.0
0 ← score on a 100-point scale → 100 · higher is better
View all reported scores
A larger local tier, close on selected agent tasks.
Benchmark
DeepSeek V4 Flash-0731
Claude Opus 4.8
Terminal Bench 2.1
82.7
85.0
DeepSWE
54.4
58.0
NL2Repo
54.2
69.7
Vendor-reported, not independently reproduced here. DeepSeek specifies its minimal Harness at max effort for public code-agent tests. The gap varies substantially by task; these figures are not a promise of identical local results. Source: DeepSeek-V4-Flash-0731 official model card ↗.
+READ THE COMPARISON CORRECTLY
Strong evidence. Specific claims.
These selected results show competitive performance on particular tests. They do not establish general parity with every frontier model, or predict how a smaller quantized checkpoint will perform on your documents and workflows.
The comparisons retain the versions named by their sources: Claude Sonnet 4.5, GPT-5 mini, and Claude Opus 4.8. They are not automatically comparisons with the latest model in each product.
What about GPT-5.6 Terra?
The reviewed OpenAI GPT-5.6 Terra documentation ↗ does not supply a matching score for these tables. We have left that comparison unscored instead of relabeling another GPT model. A task-specific evaluation can compare an available hosted model with the exact local configuration you are considering.
+THREE USEFUL TIERS
Choose the fit. Then prove it.
WORKSTATION CANDIDATE
Qwen3.5-27B
A dense vision-language model to evaluate for document understanding, reasoning, and assisted coding. Quantization can make it practical on a capable workstation; long context and multiple users need additional memory.
A mixture-of-experts family with a much larger total weight footprint. The 0731 checkpoint includes DSpark components and lists 304B parameters; active parameters do not tell you how much memory is needed to store the model.
Its official serving example uses four GB300 GPUs. This belongs in a server-sizing discussion, with runtime compatibility and load testing.
Small text models for constrained assistants and narrow local tasks. Meta’s 3B Instruct BF16 results include MMLU 63.4, IFEval 77.4, and GSM8K 77.7.
This older, smaller tier is useful for its footprint. Those tests and settings differ from the charts above; they are not a frontier-parity claim. Review Meta’s license and use policy for the intended deployment.
Use representative documents, permitted and forbidden requests, expected answer formats, real tools, and realistic concurrency. Compare quality, groundedness, task completion, latency, and cost together.