Coding
Like-for-like- Gemini 3.5 Flash
- 55.1
- GPT-5.6 Luna
- 62.7
- Weighted basis
- 1 vs 1 rows
- Reading
- GPT-5.6 Luna leads
Model comparison
Gemini 3.5 Flash vs GPT-5.6 Luna
Updated August 10, 2026. Public scores include evidence status and uncertainty. They are not guarantees for a specific workload.
GPT-5.6 Luna has the higher public score estimate, 66.87 versus 64.06, but the 90% score intervals overlap. Treat that as a lead, not a settled winner.
10 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
Chat turn cost
1K fresh input + 500 output tokens
GPT-5.6 Luna
GPT-5.6 Luna has the lower estimated token cost for this stated workload. Costs use the listed standard API rates.
Confidence: listed-rates
Show secondary and unsupported calls
Cache-heavy agent loop cost
200K cached + 20K fresh input + 10K output tokens
GPT-5.6 Luna
GPT-5.6 Luna has the lower estimated token cost for this stated workload. Costs use the listed standard API rates.
Confidence: listed-rates
Repository review cost
50K fresh input + 3K output tokens
GPT-5.6 Luna
GPT-5.6 Luna has the lower estimated token cost for this stated workload. Costs use the listed standard API rates.
Confidence: listed-rates
Agentic work
Tool use, computer use, and multi-step task completion
Not enough matched evidence
The category averages use different weighted benchmark sets, so they are directional rather than like-for-like.
Confidence: limited
What is actually comparable
Shared results can support a head-to-head reading. Results present for only one model describe coverage, not superiority.
12 Gemini 3.5 Flash only
10 Shared
12 GPT-5.6 Luna only
- Shared results
- 10
- Gemini 3.5 Flash only
- 12
- GPT-5.6 Luna only
- 12
- Like-for-like categories
- 2 / 8
3 categories use 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.
Math
Like-for-like- Gemini 3.5 Flash
- 32.9
- GPT-5.6 Luna
- 73.6
- Weighted basis
- 2 vs 2 rows
- Reading
- GPT-5.6 Luna leads
Agentic
Directional only- Gemini 3.5 Flash
- 77.2
- GPT-5.6 Luna
- 84.1
- Weighted basis
- 2 vs 2 rows
- Reading
- Directional only
Reasoning
Directional only- Gemini 3.5 Flash
- 74.7
- GPT-5.6 Luna
- 59.5
- Weighted basis
- 2 vs 1 rows
- Reading
- Directional only
Multimodal
Directional only- Gemini 3.5 Flash
- 83.8
- GPT-5.6 Luna
- 78.4
- Weighted basis
- 2 vs 1 rows
- Reading
- Directional only
Knowledge
Not comparable- Gemini 3.5 Flash
- 40.2
- GPT-5.6 Luna
- 92.3
- Weighted basis
- 1 vs 1 rows
- Reading
- Not comparable
Multilingual
Not comparable- Gemini 3.5 Flash
- Not measured
- GPT-5.6 Luna
- Not measured
- Weighted basis
- 0 vs 0 rows
- Reading
- Not comparable
Instruction following
Not comparable- Gemini 3.5 Flash
- Not measured
- GPT-5.6 Luna
- Not measured
- Weighted basis
- 0 vs 0 rows
- Reading
- Not comparable
| Category | Gemini 3.5 Flash | GPT-5.6 Luna | Weighted basis | Reading |
|---|---|---|---|---|
| Coding | 55.1 | 62.7 | Like-for-like1 vs 1 rows | GPT-5.6 Luna leads |
| Math | 32.9 | 73.6 | Like-for-like2 vs 2 rows | GPT-5.6 Luna leads |
| Agentic | 77.2 | 84.1 | Directional only2 vs 2 rows | Directional only |
| Reasoning | 74.7 | 59.5 | Directional only2 vs 1 rows | Directional only |
| Multimodal | 83.8 | 78.4 | Directional only2 vs 1 rows | Directional only |
| Knowledge | 40.2 | 92.3 | Not comparable1 vs 1 rows | Not comparable |
| Multilingual | Not measured | Not measured | Not comparable0 vs 0 rows | Not comparable |
| Instruction following | Not measured | Not measured | Not comparable0 vs 0 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.
FrontierMath v2 (Tier 4)
Math
Gemini 3.5 Flash: 14.583%GPT-5.6 Luna: 58.500%Normalized gap 43.9Gemini 3.5 Flash source GPT-5.6 Luna sourceFrontierMath v2 (Tiers 1-3)
Math
Gemini 3.5 Flash: 38.966%GPT-5.6 Luna: 78.600%Normalized gap 39.6Gemini 3.5 Flash source GPT-5.6 Luna sourceARC-AGI-2
Reasoning
Gemini 3.5 Flash: 72.1%GPT-5.6 Luna: 59.5%Normalized gap 12.6Gemini 3.5 Flash source GPT-5.6 Luna sourceTerminal-Bench 2.0
Agentic
Gemini 3.5 Flash: 76.2%GPT-5.6 Luna: 84.7%Normalized gap 8.5Gemini 3.5 Flash source GPT-5.6 Luna sourceSWE-bench Pro
Coding
Gemini 3.5 Flash: 55.1%GPT-5.6 Luna: 62.7%Normalized gap 7.6Gemini 3.5 Flash source GPT-5.6 Luna source
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
- Gemini 3.5 Flash
- $0.006
- Fits in one request
- GPT-5.6 Luna
- $0.0008
- Fits in one request
GPT-5.6 Luna has the lower modeled cost
Costs use the listed standard API rates.
Repository review
50K fresh input + 3K output tokens
- Gemini 3.5 Flash
- $0.102
- Fits in one request
- GPT-5.6 Luna
- $0.0136
- Fits in one request
GPT-5.6 Luna has the lower modeled cost
Costs use the listed standard API rates.
Cache-heavy agent loop
200K cached + 20K fresh input + 10K output tokens
- Gemini 3.5 Flash
- $0.15
- Fits in one request
- GPT-5.6 Luna
- $0.02
- Fits in one request
GPT-5.6 Luna has the lower modeled cost
Costs use the listed standard API rates.
Specification differences
Sourced differences are shown directly. Missing facts stay explicit instead of being inferred from a model name or family.
SpecificationGemini 3.5 FlashGPT-5.6 Luna
Context window
Maximum documented context; output-token limits may be lower.
Gemini 3.5 Flash
GPT-5.6 Luna
1.05M
OpenAI model catalogAPI model ID
Gemini 3.5 Flash
gemini-3.5-flash
Google Gemini API pricingGPT-5.6 Luna
gpt-5.6-luna
OpenAI model catalogCached-input rate
A missing cached-input rate falls back to the listed input rate only in the stated workload estimate.
Gemini 3.5 Flash
$0.15 per 1M cached input tokens
Google Gemini API pricingGPT-5.6 Luna
$0.02 per 1M cached input tokens
OpenAI pricingDocumented inputs
Gemini 3.5 Flash
Not sourced
GPT-5.6 Luna
text, image
OpenAI model catalogDocumented outputs
Gemini 3.5 Flash
Not sourced
GPT-5.6 Luna
Provider availability
Gemini 3.5 Flash
Not sourced
GPT-5.6 Luna
Generally Available · OpenAI Responses API
OpenAI model catalogReasoning profile
Gemini 3.5 Flash
Reasoning
GPT-5.6 Luna
Reasoning
Weight access
Gemini 3.5 Flash
Proprietary
GPT-5.6 Luna
Proprietary
License
Gemini 3.5 Flash
Proprietary
GPT-5.6 Luna
Proprietary
Release date
Gemini 3.5 Flash
2026-05-19
GPT-5.6 Luna
2026-07-09
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-5.6 Luna has the higher public score estimate, 66.87 versus 64.06, but the 90% score intervals overlap.
- Workload cost
- Repository review: $0.102 vs $0.0136. Cache-heavy agent loop: $0.15 vs $0.02.
- 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 evidence34 rows
Agentic
Terminal-Bench 2.0
Gemini 3.5 Flash76.2%SourceGPT-5.6 Luna84.7%SourceGPT-5.6 Luna leads this result
MCP Atlas
Gemini 3.5 Flash83.6%SourceGPT-5.6 Luna—Not directly comparable
Toolathlon
Gemini 3.5 Flash56.5%SourceGPT-5.6 Luna53.4%SourceGemini 3.5 Flash leads this result
OSWorld-Verified
Gemini 3.5 Flash78.4%SourceGPT-5.6 Luna—Not directly comparable
Finance Agent v2
Gemini 3.5 Flash57.9%SourceGPT-5.6 Luna—Not directly comparable
Gert Labs
Gemini 3.5 Flash61.85%SourceGPT-5.6 Luna—Not directly comparable
ResearchClawBench
Gemini 3.5 Flash18.0%SourceGPT-5.6 Luna—Not directly comparable
BrowseComp
Gemini 3.5 Flash—GPT-5.6 Luna83.3%SourceNot directly comparable
OSWorld 2.0
Gemini 3.5 Flash—GPT-5.6 Luna45.6%SourceNot directly comparable
CyberGym
Gemini 3.5 Flash—GPT-5.6 Luna77.9%SourceNot directly comparable
ExploitGym
Gemini 3.5 Flash—GPT-5.6 Luna12.4%SourceNot directly comparable
Coding
Terminal-Bench 2.0
Gemini 3.5 Flash76.2%SourceGPT-5.6 Luna84.7%SourceGPT-5.6 Luna leads this result
SWE-bench Pro
Gemini 3.5 Flash55.1%SourceGPT-5.6 Luna62.7%SourceGPT-5.6 Luna leads this result
Vibe Code Bench
Gemini 3.5 Flash48.68%SourceGPT-5.6 Luna—Not directly comparable
cursorBench31
Gemini 3.5 Flash49.8%SourceGPT-5.6 Luna—Not directly comparable
cursorBench32
Shared sourceGemini 3.5 Flash48.8%GPT-5.6 Luna61.1%GPT-5.6 Luna leads this result
deepSwe
Gemini 3.5 Flash—GPT-5.6 Luna67.2%SourceNot directly comparable
FrontierCode 1.1 Extended
Gemini 3.5 Flash—GPT-5.6 Luna55.1%SourceNot directly comparable
Reasoning
MRCRv2
Gemini 3.5 Flash77.3%SourceGPT-5.6 Luna—Not directly comparable
MRCR 1M
Gemini 3.5 Flash26.6%SourceGPT-5.6 Luna—Not directly comparable
ARC-AGI-2
Gemini 3.5 Flash72.1%SourceGPT-5.6 Luna59.5%SourceGemini 3.5 Flash leads this result
ARC-AGI-3
Gemini 3.5 Flash—GPT-5.6 Luna0.2%SourceNot directly comparable
Knowledge
GPQA-D
Gemini 3.5 Flash92.7%SourceGPT-5.6 Luna92.3%SourceGemini 3.5 Flash leads this result
HLE
Gemini 3.5 Flash40.2%SourceGPT-5.6 Luna—Not directly comparable
GPQA
Gemini 3.5 Flash—GPT-5.6 Luna92.3%SourceNot directly comparable
HealthBench Professional
Gemini 3.5 Flash—GPT-5.6 Luna55.7%SourceNot directly comparable
HealthBench Hard
Gemini 3.5 Flash—GPT-5.6 Luna32.0%SourceNot directly comparable
Math
FrontierMath v2 (Tiers 1-3)
Gemini 3.5 Flash38.966%SourceGPT-5.6 Luna78.600%SourceGPT-5.6 Luna leads this result
FrontierMath v2 (Tier 4)
Gemini 3.5 Flash14.583%SourceGPT-5.6 Luna58.500%SourceGPT-5.6 Luna leads this result
FrontierMath (legacy)
Gemini 3.5 Flash—GPT-5.6 Luna78.6%SourceNot directly comparable
Multimodal
CharXiv
Gemini 3.5 Flash84.2%SourceGPT-5.6 Luna—Not directly comparable
MMMU-Pro
Gemini 3.5 Flash83.6%SourceGPT-5.6 Luna78.4%SourceGemini 3.5 Flash leads this result
Blueprint-Bench 2
Gemini 3.5 Flash33.6%SourceGPT-5.6 Luna—Not directly comparable
MMMU-Pro w/ Python
Gemini 3.5 Flash—GPT-5.6 Luna79.5%SourceNot directly comparable
Frequently asked questions
Which is better, Gemini 3.5 Flash or GPT-5.6 Luna?
GPT-5.6 Luna has the higher public score estimate, 66.87 versus 64.06, but the 90% score intervals overlap. The higher estimate is not a decisive winner because the uncertainty ranges overlap.
Which is better for coding, Gemini 3.5 Flash or GPT-5.6 Luna?
GPT-5.6 Luna leads the like-for-like coding comparison across 1 shared weighted benchmark row.
Which is better for agentic tasks, Gemini 3.5 Flash or GPT-5.6 Luna?
The current agentic tasks averages use different weighted benchmark sets, so BenchLM does not name a winner from them. Read the shared benchmark rows directly and test the models on the same task set.
Which costs less, Gemini 3.5 Flash or GPT-5.6 Luna?
For the stated presets, chat costs $0.006 on Gemini 3.5 Flash and $0.0008 on GPT-5.6 Luna; repository review costs $0.102 and $0.0136; the cache-heavy agent loop costs $0.15 and $0.02. Costs use the listed standard API rates.
Which has the larger context window, Gemini 3.5 Flash or GPT-5.6 Luna?
GPT-5.6 Luna has the larger documented context window: 1.05M, compared with 1M.
Related comparisons
Last updated August 10, 2026
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