Coding
Like-for-like- GPT-5.6 Luna
- 62.7
- Qwen3.8-27B
- 61.7
- Weighted basis
- 1 vs 1 rows
- Reading
- GPT-5.6 Luna leads
GPT-5.6 Luna vs Qwen3.8-27B
Updated August 14, 2026. Public scores include evidence status and uncertainty. They are not guarantees for a specific workload.
Decision reading
At least one model is not scored in the current public ranking lane, so the page does not name an overall quality winner.
4 results are shared. Category rows based on different benchmark sets are marked directional and do not name a winner.
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-5.6 Luna
GPT-5.6 Luna leads on the same 1 weighted benchmark row.
Confidence: limited
Long documents
Prompts that approach the documented context limit
GPT-5.6 Luna
GPT-5.6 Luna 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
No shared weighted benchmark basis supports a winner.
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.
19 GPT-5.6 Luna only
4 Shared
23 Qwen3.8-27B only
- Shared results
- 4
- GPT-5.6 Luna only
- 19
- Qwen3.8-27B only
- 23
- Like-for-like categories
- 1 / 8
1 category uses different evidence sets. Those rows remain visible for coverage context but do not name a winner.
Category results, on a stated basis
Each row states whether both averages use the same weighted benchmark set. Directional and not-comparable rows remain visible, but they never receive a winner in this template.
Knowledge
Directional only- GPT-5.6 Luna
- 92.3
- Qwen3.8-27B
- 38.7
- Weighted basis
- 1 vs 2 rows
- Reading
- Directional only
Agentic
Not comparable- GPT-5.6 Luna
- 84.1
- Qwen3.8-27B
- 84.3
- Weighted basis
- 2 vs 1 rows
- Reading
- Not comparable
Reasoning
Not comparable- GPT-5.6 Luna
- 59.5
- Qwen3.8-27B
- Not measured
- Weighted basis
- 1 vs 0 rows
- Reading
- Not comparable
Math
Not comparable- GPT-5.6 Luna
- 73.6
- Qwen3.8-27B
- Not measured
- Weighted basis
- 2 vs 0 rows
- Reading
- Not comparable
Multilingual
Not comparable- GPT-5.6 Luna
- Not measured
- Qwen3.8-27B
- Not measured
- Weighted basis
- 0 vs 0 rows
- Reading
- Not comparable
Multimodal
Not comparable- GPT-5.6 Luna
- 78.4
- Qwen3.8-27B
- 90.2
- Weighted basis
- 1 vs 1 rows
- Reading
- Not comparable
Instruction following
Not comparable- GPT-5.6 Luna
- Not measured
- Qwen3.8-27B
- 79.5
- Weighted basis
- 0 vs 1 rows
- Reading
- Not comparable
| Category | GPT-5.6 Luna | Qwen3.8-27B | Weighted basis | Reading |
|---|---|---|---|---|
| Coding | 62.7 | 61.7 | Like-for-like1 vs 1 rows | GPT-5.6 Luna leads |
| Knowledge | 92.3 | 38.7 | Directional only1 vs 2 rows | Directional only |
| Agentic | 84.1 | 84.3 | Not comparable2 vs 1 rows | Not comparable |
| Reasoning | 59.5 | Not measured | Not comparable1 vs 0 rows | Not comparable |
| Math | 73.6 | Not measured | Not comparable2 vs 0 rows | Not comparable |
| Multilingual | Not measured | Not measured | Not comparable0 vs 0 rows | Not comparable |
| Multimodal | 78.4 | 90.2 | Not comparable1 vs 1 rows | Not comparable |
| Instruction following | Not measured | 79.5 | Not comparable0 vs 1 rows | Not comparable |
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.
GPQA
Knowledge
SWE-bench Pro
Coding
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-5.6 Luna
- $0.0008
- Fits in one request
- Qwen3.8-27B
- Self-hosted; infrastructure cost varies
- Fits in one request
Qwen3.8-27B has no comparable published API token rate.
Repository review
50K fresh input + 3K output tokens
- GPT-5.6 Luna
- $0.0136
- Fits in one request
- Qwen3.8-27B
- Self-hosted; infrastructure cost varies
- Fits in one request
Qwen3.8-27B has no comparable published API token rate.
Cache-heavy agent loop
200K cached + 20K fresh input + 10K output tokens
- GPT-5.6 Luna
- $0.02
- Fits in one request
- Qwen3.8-27B
- Self-hosted; infrastructure cost varies
- Fits in one request
- Cached-input rate unavailable
Qwen3.8-27B 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-5.6 LunaQwen3.8-27B
Context window
Maximum documented context; output-token limits may be lower.
GPT-5.6 Luna
1.05M
OpenAI model catalogQwen3.8-27B
API model ID
GPT-5.6 Luna
gpt-5.6-luna
OpenAI model catalogQwen3.8-27B
Not sourced
Cached-input rate
A missing cached-input rate falls back to the listed input rate only in the stated workload estimate.
GPT-5.6 Luna
$0.02 per 1M cached input tokens
OpenAI pricingQwen3.8-27B
No comparable hosted API rate
Qwen3.8-27B model cardDocumented inputs
GPT-5.6 Luna
text, image
OpenAI model catalogQwen3.8-27B
Not sourced
Documented outputs
GPT-5.6 Luna
Qwen3.8-27B
Not sourced
Provider availability
GPT-5.6 Luna
Generally Available · OpenAI Responses API
OpenAI model catalogQwen3.8-27B
Not sourced
Reasoning profile
GPT-5.6 Luna
Reasoning
Qwen3.8-27B
Reasoning
Weight access
GPT-5.6 Luna
Proprietary
Qwen3.8-27B
Open Weight
License
GPT-5.6 Luna
Proprietary
Qwen3.8-27B
Open Weight
Release date
GPT-5.6 Luna
2026-07-09
Qwen3.8-27B
2026-08-05
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
- At least one model is not scored in the current public ranking lane, so the page does not name an overall quality winner.
- Workload cost
- A complete comparable API-rate estimate is not available for both models.
- Context tradeoff
- GPT-5.6 Luna has the larger documented window (1.05M).
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 evidence46 rows
Agentic
Terminal-Bench 3.0
GPT-5.6 Luna14.3%SourceQwen3.8-27B—Not directly comparable
Terminal-Bench 2.0
GPT-5.6 Luna84.7%SourceQwen3.8-27B—Not directly comparable
BrowseComp
GPT-5.6 Luna83.3%SourceQwen3.8-27B—Not directly comparable
OSWorld 2.0
GPT-5.6 Luna45.6%SourceQwen3.8-27B—Not directly comparable
CyberGym
GPT-5.6 Luna77.9%SourceQwen3.8-27B—Not directly comparable
ExploitGym
GPT-5.6 Luna12.4%SourceQwen3.8-27B—Not directly comparable
Toolathlon
GPT-5.6 Luna53.4%SourceQwen3.8-27B—Not directly comparable
Terminal-Bench 2.1
GPT-5.6 Luna—Qwen3.8-27B73.0%SourceNot directly comparable
CoWorkBench
GPT-5.6 Luna—Qwen3.8-27B70.7%SourceNot directly comparable
JobBench
GPT-5.6 Luna—Qwen3.8-27B33.4%SourceNot directly comparable
Agents' Last Exam
GPT-5.6 Luna—Qwen3.8-27B42.9%SourceNot directly comparable
OSWorld-Verified
GPT-5.6 Luna—Qwen3.8-27B84.3%SourceNot directly comparable
WebArena-Verified
GPT-5.6 Luna—Qwen3.8-27B64.8%SourceNot directly comparable
AndroidWorld
GPT-5.6 Luna—Qwen3.8-27B81.9%SourceNot directly comparable
Coding
SWE-bench Pro
GPT-5.6 Luna62.7%SourceQwen3.8-27B61.7%SourceGPT-5.6 Luna leads this result
Terminal-Bench 2.0
GPT-5.6 Luna84.7%SourceQwen3.8-27B—Not directly comparable
deepSwe
GPT-5.6 Luna67.2%SourceQwen3.8-27B42.2%SourceGPT-5.6 Luna leads this result
FrontierCode 1.1 Extended
GPT-5.6 Luna55.1%SourceQwen3.8-27B—Not directly comparable
cursorBench32
GPT-5.6 Luna61.1%SourceQwen3.8-27B—Not directly comparable
Terminal-Bench 2.1
GPT-5.6 Luna—Qwen3.8-27B73.0%SourceNot directly comparable
NL2Repo
GPT-5.6 Luna—Qwen3.8-27B42.3%SourceNot directly comparable
LiveCodeBench v6
GPT-5.6 Luna—Qwen3.8-27B90.3%SourceNot directly comparable
Reasoning
Knowledge
GPQA
GPT-5.6 Luna92.3%SourceQwen3.8-27B89.2%SourceGPT-5.6 Luna leads this result
GPQA-D
GPT-5.6 Luna92.3%SourceQwen3.8-27B89.2%SourceGPT-5.6 Luna leads this result
HealthBench Professional
GPT-5.6 Luna55.7%SourceQwen3.8-27B—Not directly comparable
HealthBench Hard
GPT-5.6 Luna32.0%SourceQwen3.8-27B—Not directly comparable
HLE
GPT-5.6 Luna—Qwen3.8-27B30.8%SourceNot directly comparable
HLE w/o tools
GPT-5.6 Luna—Qwen3.8-27B30.8%SourceNot directly comparable
Math
Multimodal
MMMU-Pro
GPT-5.6 Luna78.4%SourceQwen3.8-27B—Not directly comparable
MMMU-Pro w/ Python
GPT-5.6 Luna79.5%SourceQwen3.8-27B—Not directly comparable
MathVision
GPT-5.6 Luna—Qwen3.8-27B90.0%SourceNot directly comparable
MathVision w/ Python
GPT-5.6 Luna—Qwen3.8-27B94.6%SourceNot directly comparable
BabyVision
GPT-5.6 Luna—Qwen3.8-27B65.7%SourceNot directly comparable
BabyVision w/ Python
GPT-5.6 Luna—Qwen3.8-27B85.6%SourceNot directly comparable
Vision2Web
GPT-5.6 Luna—Qwen3.8-27B62.9%SourceNot directly comparable
CharXiv w/o tools
GPT-5.6 Luna—Qwen3.8-27B83.7%SourceNot directly comparable
CharXiv
GPT-5.6 Luna—Qwen3.8-27B90.2%SourceNot directly comparable
OmniDocBench 1.5
GPT-5.6 Luna—Qwen3.8-27B91.1%SourceNot directly comparable
RealWorldQA
GPT-5.6 Luna—Qwen3.8-27B85.9%SourceNot directly comparable
ERQA
GPT-5.6 Luna—Qwen3.8-27B65.5%SourceNot directly comparable
Instruction following
IFBench
GPT-5.6 Luna—Qwen3.8-27B79.5%SourceNot directly comparable
Frequently asked questions
Which is better, GPT-5.6 Luna or Qwen3.8-27B?
At least one model is not scored in the current public ranking lane, so the page does not name an overall quality winner. The page therefore keeps the decision tied to the specific documented workload.
Which is better for coding, GPT-5.6 Luna or Qwen3.8-27B?
GPT-5.6 Luna leads the like-for-like coding comparison across 1 shared weighted benchmark row.
Which is better for agentic tasks, GPT-5.6 Luna or Qwen3.8-27B?
The published evidence does not provide a shared weighted agentic tasks basis for both models, so BenchLM does not name a agentic tasks winner.
Which costs less, GPT-5.6 Luna or Qwen3.8-27B?
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-5.6 Luna or Qwen3.8-27B?
GPT-5.6 Luna has the larger documented context window: 1.05M, compared with 262K.
Related comparisons
Last updated August 14, 2026
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