Coding
Like-for-like- GPT-5.5
- 67.7
- Supported · #8/183
- Qwen3.5-27B
- 40.7
- Supported · #139/183
- Basis
- BenchAlign lane · 9 vs 2 public rows
- Reading
- GPT-5.5 leads
GPT-5.5 vs Qwen3.5-27B
Updated September 4, 2026. Public scores include evidence status and uncertainty. They are not guarantees for a specific workload.
Decision reading
GPT-5.5 has the higher public score, 73.27 versus 58.31, and the 90% score intervals do not overlap.
5 results are shared. Category rows resting on Estimated evidence or different benchmark sets are marked directional and do not name a winner.
Share or export
Which one for your work
Recommendations appear only when a shared evidence basis or an explicit operating constraint supports the call. Secondary and unsupported use cases stay disclosed below the initial list.
Coding work
Code generation, repair, and software-engineering tasks
GPT-5.5
GPT-5.5 leads on the public coding lane, 67.7 to 40.7, with Supported evidence for both models and non-overlapping 90% intervals.
Confidence: stronger
Long documents
Prompts that approach the documented context limit
GPT-5.5
GPT-5.5 has the larger documented context window.
Confidence: documented
Show secondary and unsupported calls
Agentic work
Tool use, computer use, and multi-step task completion
Not enough matched evidence
Qwen3.5-27B is scored on Estimated evidence for agentic, so the reading is directional rather than like-for-like.
Confidence: limited
Chat turn cost
1K fresh input + 500 output tokens
Not enough matched evidence
A complete comparable API-rate estimate is not available for both models.
Confidence: listed-rates
Cache-heavy agent loop cost
200K cached + 20K fresh input + 10K output tokens
Not enough matched evidence
A complete comparable API-rate estimate is not available for both models.
Confidence: listed-rates
Repository review cost
50K fresh input + 3K output tokens
Not enough matched evidence
A complete comparable API-rate estimate is not available for both models.
Confidence: listed-rates
What is actually comparable
Shared results can support a head-to-head reading. Results present for only one model describe coverage, not superiority.
33 GPT-5.5 only
5 Shared
11 Qwen3.5-27B only
- Shared results
- 5
- GPT-5.5 only
- 33
- Qwen3.5-27B only
- 11
- Like-for-like categories
- 2 / 8
2 categories rest on Estimated evidence or different evidence sets. Those rows remain visible for coverage context but do not name a winner.
Category results, on a stated basis
Each row shows the public-lane category score for both models: the BenchAlign lane for agentic, coding, and knowledge, and the weighted public rows elsewhere, with the same rank each model page reports. A row is like-for-like only when both scores rest on Supported evidence or the same weighted set. Directional and not-comparable rows remain visible, but they never receive a winner.
Knowledge
Like-for-like- GPT-5.5
- 73.3
- Supported · #7/181
- Qwen3.5-27B
- 48.5
- Supported · #100/181
- Basis
- BenchAlign lane · 6 vs 3 public rows
- Reading
- GPT-5.5 leads
Agentic
Directional only- GPT-5.5
- 63.9
- Supported · #15/151
- Qwen3.5-27B
- 48.5
- Estimated · #75/151
- Basis
- BenchAlign lane · 13 vs 4 public rows
- Reading
- Directional only
Instruction following
Directional only- GPT-5.5
- 92.9
- #7/120
- Qwen3.5-27B
- 92.6
- #13/120
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Directional only
Reasoning
Not comparable- GPT-5.5
- 63.5
- #15/22
- Qwen3.5-27B
- 50.9
- Unranked · 3 rankable rows
- Basis
- Provisional lane · 2 vs 1 weighted rows
- Reading
- Not comparable
Math
Not comparable- GPT-5.5
- 69.6
- Unranked · 3 rankable rows
- Qwen3.5-27B
- Not ranked
- Basis
- Provisional lane · 2 vs 0 weighted rows
- Reading
- Not comparable
Multilingual
Not comparable- GPT-5.5
- Not ranked
- Qwen3.5-27B
- 36.8
- #9/12
- Basis
- Provisional lane · 0 vs 1 weighted rows
- Reading
- Not comparable
Multimodal
Not comparable- GPT-5.5
- 71.3
- #19/48
- Qwen3.5-27B
- 70.5
- Unranked · 3 rankable rows
- Basis
- Provisional lane · 2 vs 0 weighted rows
- Reading
- Not comparable
| Category | GPT-5.5 | Qwen3.5-27B | Basis | Reading |
|---|---|---|---|---|
| Coding | 67.7Supported · #8/183 | 40.7Supported · #139/183 | Like-for-likeBenchAlign lane · 9 vs 2 public rows | GPT-5.5 leads |
| Knowledge | 73.3Supported · #7/181 | 48.5Supported · #100/181 | Like-for-likeBenchAlign lane · 6 vs 3 public rows | GPT-5.5 leads |
| Agentic | 63.9Supported · #15/151 | 48.5Estimated · #75/151 | Directional onlyBenchAlign lane · 13 vs 4 public rows | Directional only |
| Instruction following | 92.9#7/120 | 92.6#13/120 | Directional onlyProvisional lane · 0 vs 0 weighted rows | Directional only |
| Reasoning | 63.5#15/22 | 50.9Unranked · 3 rankable rows | Not comparableProvisional lane · 2 vs 1 weighted rows | Not comparable |
| Math | 69.6Unranked · 3 rankable rows | Not ranked | Not comparableProvisional lane · 2 vs 0 weighted rows | Not comparable |
| Multilingual | Not ranked | 36.8#9/12 | Not comparableProvisional lane · 0 vs 1 weighted rows | Not comparable |
| Multimodal | 71.3#19/48 | 70.5Unranked · 3 rankable rows | Not comparableProvisional lane · 2 vs 0 weighted rows | Not comparable |
Ranks count the models scored in each category’s lane, so the agentic, coding, and knowledge denominators (BenchAlign) differ from the provisional-lane categories. Unranked scores sit on the lane’s scale but never name a winner. Like-for-like rows within 0.5 points read as a practical tie.
Shape of the matched evidence
Only shared public evidence is shown. Sparse evidence stays a ruled list rather than being closed into a radar shape.
Too few matched category axes support a radar. The ruled list below shows only shared benchmark results; positions use each benchmark’s normalized display scale when available.
Terminal-Bench 2.0
Agentic
BrowseComp
Agentic
OSWorld-Verified
Agentic
GPQA
Knowledge
What each workload costs
Three fixed token mixes turn per-token rates into comparable decisions. Each scenario states context fit and whether cached input had to fall back to the published list-input rate.
Chat turn
1K fresh input + 500 output tokens
- GPT-5.5
- $0.02
- Fits in one request
- Qwen3.5-27B
- Self-hosted; infrastructure cost varies
- Fits in one request
Qwen3.5-27B has no comparable published API token rate.
Repository review
50K fresh input + 3K output tokens
- GPT-5.5
- $0.34
- Fits in one request
- Qwen3.5-27B
- Self-hosted; infrastructure cost varies
- Fits in one request
Qwen3.5-27B has no comparable published API token rate.
Cache-heavy agent loop
200K cached + 20K fresh input + 10K output tokens
- GPT-5.5
- $0.5
- Fits in one request
- Qwen3.5-27B
- Self-hosted; infrastructure cost varies
- Fits in one request
- Cached-input rate unavailable
Qwen3.5-27B has no comparable published API token rate.
Specification differences
Sourced differences are shown directly. Missing facts stay explicit instead of being inferred from a model name or family.
SpecificationGPT-5.5Qwen3.5-27B
Context window
Maximum documented context; output-token limits may be lower.
GPT-5.5
Qwen3.5-27B
262K
API model ID
GPT-5.5
gpt-5.5
OpenAI pricingQwen3.5-27B
Not sourced
Cached-input rate
A missing cached-input rate falls back to the listed input rate only in the stated workload estimate.
GPT-5.5
$0.5 per 1M cached input tokens
OpenAI pricingQwen3.5-27B
No comparable hosted API rate
Documented inputs
GPT-5.5
Not sourced
Qwen3.5-27B
Not sourced
Documented outputs
GPT-5.5
Not sourced
Qwen3.5-27B
Not sourced
Provider availability
GPT-5.5
Not sourced
Qwen3.5-27B
Not sourced
Reasoning profile
GPT-5.5
Reasoning
Qwen3.5-27B
Reasoning
Weight access
GPT-5.5
Proprietary
Qwen3.5-27B
Open Weight
License
GPT-5.5
Proprietary
Qwen3.5-27B
Open Weight
Release date
GPT-5.5
2026-04-23
Qwen3.5-27B
2026-03-04
If you already use one of these models
- Deployment change
- The models list different providers, so authentication, endpoint behavior, limits, and feature support may change.
- Quality signal
- GPT-5.5 has the higher public score, 73.27 versus 58.31, and the 90% score intervals do not overlap.
- Workload cost
- A complete comparable API-rate estimate is not available for both models.
- Context tradeoff
- GPT-5.5 has the larger documented window (1M).
Run the same representative tasks against both endpoints before changing production traffic.
Benchmark evidence
The full public result ledger is available for audit without forcing a wide desktop table onto a phone.
Browse raw public benchmark evidence49 rows
Agentic
Terminal-Bench 2.0
GPT-5.582%SourceQwen3.5-27B41.6%SourceGPT-5.5 leads this result
CyberGym
GPT-5.581.8%SourceQwen3.5-27B—Not directly comparable
BrowseComp
GPT-5.584.4%SourceQwen3.5-27B61%SourceGPT-5.5 leads this result
OSWorld-Verified
GPT-5.578.7%SourceQwen3.5-27B56.2%SourceGPT-5.5 leads this result
MCP Atlas
GPT-5.575.3%SourceQwen3.5-27B—Not directly comparable
Toolathlon
GPT-5.555.6%SourceQwen3.5-27B—Not directly comparable
τ²-bench results
GPT-5.598%SourceQwen3.5-27B—Not directly comparable
Gert Labs
Shared sourceGPT-5.572.93%Qwen3.5-27B39.41%GPT-5.5 leads this result
ResearchClawBench
GPT-5.517.0%SourceQwen3.5-27B—Not directly comparable
OSWorld 2.0
GPT-5.513.0%SourceQwen3.5-27B—Not directly comparable
JobBench
GPT-5.542.7%SourceQwen3.5-27B—Not directly comparable
ExploitGym
GPT-5.513.4%SourceQwen3.5-27B—Not directly comparable
Terminal-Bench 2.1 (Vals)
GPT-5.576.4%SourceQwen3.5-27B—Not directly comparable
Coding
SWE-bench Pro
GPT-5.558.6%SourceQwen3.5-27B—Not directly comparable
Terminal-Bench 2.0
GPT-5.582.0%SourceQwen3.5-27B—Not directly comparable
Vibe Code Bench
GPT-5.569.85%SourceQwen3.5-27B—Not directly comparable
React Native Evals
GPT-5.584.7%SourceQwen3.5-27B—Not directly comparable
cursorBench31
GPT-5.559.2%SourceQwen3.5-27B—Not directly comparable
cursorBench32
GPT-5.558.4%SourceQwen3.5-27B—Not directly comparable
FrontierCode 1.1 Main
GPT-5.543.0%SourceQwen3.5-27B—Not directly comparable
LiveCodeBench (Vals)
GPT-5.585.3%SourceQwen3.5-27B—Not directly comparable
SWE-bench (Vals)
GPT-5.582.6%SourceQwen3.5-27B—Not directly comparable
SWE-bench Verified
GPT-5.5—Qwen3.5-27B72.4%SourceNot directly comparable
SWE-Rebench
GPT-5.5—Qwen3.5-27B58.9%SourceNot directly comparable
Reasoning
MRCR v2 64K-128K
GPT-5.583.1%SourceQwen3.5-27B—Not directly comparable
MRCR v2 128K-256K
GPT-5.587.5%SourceQwen3.5-27B—Not directly comparable
ARC-AGI-2
GPT-5.585%SourceQwen3.5-27B—Not directly comparable
ARC-AGI-3
GPT-5.50.4%SourceQwen3.5-27B—Not directly comparable
LongBench v2
GPT-5.5—Qwen3.5-27B60.6%SourceNot directly comparable
Knowledge
GPQA
GPT-5.593.6%SourceQwen3.5-27B85.5%SourceGPT-5.5 leads this result
GPQA-D
GPT-5.593.6%SourceQwen3.5-27B—Not directly comparable
HLE
GPT-5.552.2%SourceQwen3.5-27B—Not directly comparable
HLE w/o tools
GPT-5.541.4%SourceQwen3.5-27B—Not directly comparable
GPQA Diamond (Vals)
GPT-5.593.2%SourceQwen3.5-27B—Not directly comparable
MMLU-Pro (Vals)
GPT-5.588.1%SourceQwen3.5-27B—Not directly comparable
MMLU-Pro
GPT-5.5—Qwen3.5-27B86.1%SourceNot directly comparable
SuperGPQA
GPT-5.5—Qwen3.5-27B65.6%SourceNot directly comparable
Math
Multilingual
MMLU-ProX
GPT-5.5—Qwen3.5-27B82.2%SourceNot directly comparable
Multimodal
MMMU-Pro
GPT-5.581.2%SourceQwen3.5-27B—Not directly comparable
MMMU-Pro w/ Python
GPT-5.583.2%SourceQwen3.5-27B—Not directly comparable
OfficeQA Pro
GPT-5.554.1%SourceQwen3.5-27B—Not directly comparable
MMMU
GPT-5.5—Qwen3.5-27B82.3%SourceNot directly comparable
MMVU
GPT-5.5—Qwen3.5-27B73.3%SourceNot directly comparable
MathVision
GPT-5.5—Qwen3.5-27B86.0%SourceNot directly comparable
V*
GPT-5.5—Qwen3.5-27B93.7%SourceNot directly comparable
Instruction following
IFEval
GPT-5.5—Qwen3.5-27B95%SourceNot directly comparable
Frequently asked questions
Which is better, GPT-5.5 or Qwen3.5-27B?
GPT-5.5 has the higher public score, 73.27 versus 58.31, and the 90% score intervals do not overlap. That is the clearest overall quality signal in the current public data.
Which is better for coding, GPT-5.5 or Qwen3.5-27B?
GPT-5.5 leads the public coding lane, 67.7 to 40.7, with Supported evidence for both models and non-overlapping 90% intervals.
Which is better for agentic tasks, GPT-5.5 or Qwen3.5-27B?
GPT-5.5 scores higher for agentic tasks on the public lane, 63.9 to 48.5. Qwen3.5-27B is scored on Estimated evidence for agentic tasks, so the reading is directional rather than like-for-like. BenchLM does not name a winner for agentic tasks from a directional reading; read the shared benchmark rows directly and test the models on the same task set.
Which costs less, GPT-5.5 or Qwen3.5-27B?
Both models do not have comparable published API token rates, so this page does not name a universal price winner.
Which has the larger context window, GPT-5.5 or Qwen3.5-27B?
GPT-5.5 has the larger documented context window: 1M, compared with 262K.
Related comparisons
Last updated September 4, 2026
Watch GPT-5.5 vs Qwen3.5-27B
One weekly email when material rank, price, or benchmark evidence changes make this matchup worth revisiting.
Read a sample issueJoin 2,000+ readers.