Coding work
Code generation, repair, and software-engineering tasks
GPT-5.6 Luna
GPT-5.6 Luna leads on the public coding lane, 64.5 to 34.3, with Supported evidence for both models and non-overlapping 90% intervals.
Confidence: stronger
Gemma 4 26B A4B vs GPT-5.6 Luna
Updated September 24, 2026. Rank says GPT-5.6 Luna is ahead. Price, access, and your workload can each overturn that. Public scores include evidence status and uncertainty.
Both of these models will change. Get the price, version and retirement notices for the pair, each with its source. Follow model changes
Share or export
Decision reading
GPT-5.6 Luna has the higher public score estimate, 65.6 versus 46.28, but the 90% score intervals overlap. Treat that as a lead, not a settled winner. 1 results are shared. Category rows resting on Estimated evidence or different benchmark sets are marked directional and do not name a winner.
- Shared results
- 1
- Gemma 4 26B A4B only
- 3
- GPT-5.6 Luna only
- 28
- Like-for-like categories
- 3 / 8
Supported: Gemma 4 26B A4B and GPT-5.6 LunaHow the comparison works
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.
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
Gemma 4 26B A4B is not ranked on the public lane for agentic, so no winner is named for agentic.
Confidence: limitedChat 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-ratesCache-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-ratesRepository 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
Which one for a specific job
Choose a job from the LLM Selector's task catalog to see the category row it rests on, under the same basis rules as the table below. A directional row stays directional; choosing a job never creates a winner.
The same task catalog as the LLM Selector. Each job names the evidence surface it rests on; nothing here adds a new score.
34.3Gemma 4 26B A4B64.5GPT-5.6 Luna
Like-for-like · BenchAlign v5.7
GPT-5.6 Luna leads the like-for-like coding row.
Coding scores combine specific tasks and setups. Match the editor, harness, and effort to your workflow.
Same basis rules as the category table below
What is actually comparable
Shared results can support a head-to-head reading. Results present for only one model describe coverage, not superiority.
3 Gemma 4 26B A4B only
1 Shared
28 GPT-5.6 Luna only
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.
MMMU-ProMultimodal
Normalized gap 4.6Gemma 4 26B A4B:73.8%GPT-5.6 Luna:78.4%
Bars run 0–100 on each benchmark’s normalized display scale
Category results, on a stated basis
Each row shows the public-lane category score for both models: the BenchAlign v5.7 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.
Coding
Like-for-like- Gemma 4 26B A4B
- 34.3
- Supported · #77/135
- GPT-5.6 Luna
- 64.5
- Supported · #9/135
- Basis
- BenchAlign v5.7 lane · 0 vs 7 public rows
- Reading
- GPT-5.6 Luna leads
Multimodal
Like-for-like- Gemma 4 26B A4B
- 44.2
- #43/50
- GPT-5.6 Luna
- 67.1
- #22/50
- Basis
- Provisional lane · 1 vs 1 weighted rows
- Reading
- GPT-5.6 Luna leads
Knowledge
Like-for-like- Gemma 4 26B A4B
- 36.4
- Supported · #101/158
- GPT-5.6 Luna
- 64.6
- Supported · #22/158
- Basis
- BenchAlign v5.7 lane · 3 vs 6 public rows
- Reading
- GPT-5.6 Luna leads
Agentic
Not comparable- Gemma 4 26B A4B
- Not ranked
- GPT-5.6 Luna
- 55.3
- Supported · #28/105
- Basis
- BenchAlign v5.7 lane · 0 vs 9 public rows
- Reading
- Not comparable
Reasoning
Not comparable- Gemma 4 26B A4B
- 67.2
- Unranked · 2 rankable rows
- GPT-5.6 Luna
- 54.7
- #18/19
- Basis
- Provisional lane · 0 vs 2 weighted rows
- Reading
- Not comparable
Multilingual
Not comparable- Gemma 4 26B A4B
- Not ranked
- GPT-5.6 Luna
- Not ranked
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Not comparable
Instruction following
Not comparable- Gemma 4 26B A4B
- 87.3
- #31/124
- GPT-5.6 Luna
- Not ranked
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Not comparable
Math
Not comparable- Gemma 4 26B A4B
- Not ranked
- GPT-5.6 Luna
- 94.2
- Unranked · 3 rankable rows
- Basis
- Provisional lane · 0 vs 2 weighted rows
- Reading
- Not comparable
| Category | Gemma 4 26B A4B | GPT-5.6 Luna | Basis | Reading |
|---|---|---|---|---|
| Coding | 34.3Supported · #77/135 | 64.5Supported · #9/135 | Like-for-likeBenchAlign v5.7 lane · 0 vs 7 public rows | GPT-5.6 Luna leads |
| Multimodal | 44.2#43/50 | 67.1#22/50 | Like-for-likeProvisional lane · 1 vs 1 weighted rows | GPT-5.6 Luna leads |
| Knowledge | 36.4Supported · #101/158 | 64.6Supported · #22/158 | Like-for-likeBenchAlign v5.7 lane · 3 vs 6 public rows | GPT-5.6 Luna leads |
| Agentic | Not ranked | 55.3Supported · #28/105 | Not comparableBenchAlign v5.7 lane · 0 vs 9 public rows | Not comparable |
| Reasoning | 67.2Unranked · 2 rankable rows | 54.7#18/19 | Not comparableProvisional lane · 0 vs 2 weighted rows | Not comparable |
| Multilingual | Not ranked | Not ranked | Not comparableProvisional lane · 0 vs 0 weighted rows | Not comparable |
| Instruction following | 87.3#31/124 | Not ranked | Not comparableProvisional lane · 0 vs 0 weighted rows | Not comparable |
| Math | Not ranked | 94.2Unranked · 3 rankable rows | Not comparableProvisional lane · 0 vs 2 weighted rows | Not comparable |
Ranks count the models scored in each category’s lane, so the agentic, coding, and knowledge denominators (BenchAlign v5.7) differ from the provisional-lane categories. Unranked scores sit on the provisional lane’s scale but never name a winner. Like-for-like rows within 0.5 points read as a practical tie.
Supported evidence per lane · bars run 0–100Methodology
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
- Gemma 4 26B A4B
- Self-hosted; infrastructure cost varies
- Fits in one request
- GPT-5.6 Luna
- $0.0008
- Fits in one request
Gemma 4 26B A4B has no comparable published API token rate.
Repository review
50K fresh input + 3K output tokens
- Gemma 4 26B A4B
- Self-hosted; infrastructure cost varies
- Fits in one request
- GPT-5.6 Luna
- $0.0136
- Fits in one request
Gemma 4 26B A4B has no comparable published API token rate.
Cache-heavy agent loop
200K cached + 20K fresh input + 10K output tokens
- Gemma 4 26B A4B
- Self-hosted; infrastructure cost varies
- Fits in one request
- Cached-input rate unavailable
- GPT-5.6 Luna
- $0.02
- Fits in one request
Gemma 4 26B A4B has no comparable published API token rate.
Cached input falls back to the list input rate only where a cached rate is unpublished
Specification differences
Sourced differences are shown directly. Missing facts stay explicit instead of being inferred from a model name or family.
SpecificationGemma 4 26B A4BGPT-5.6 Luna
Context window
Maximum documented context; output-token limits may be lower.
Gemma 4 26B A4B
GPT-5.6 Luna
1.05M
OpenAI model catalogAPI model ID
Gemma 4 26B A4B
gemma-4-26b-a4b-it
Google Gemma Gemini API guideGPT-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.
Gemma 4 26B A4B
No comparable hosted API rate
GPT-5.6 Luna
$0.02 per 1M cached input tokens
OpenAI pricingDocumented inputs
Gemma 4 26B A4B
text, image
Google Gemma 4 model documentationGPT-5.6 Luna
text, image
OpenAI model catalogDocumented outputs
Gemma 4 26B A4B
GPT-5.6 Luna
Provider availability
Gemma 4 26B A4B
Generally Available · Gemini API, Google AI Studio, open weights
Google Gemma Gemini API guideGPT-5.6 Luna
Generally Available · OpenAI Responses API
OpenAI model catalogReasoning profile
Gemma 4 26B A4B
Reasoning
GPT-5.6 Luna
Reasoning
Weight access
Gemma 4 26B A4B
Open Weight
GPT-5.6 Luna
Proprietary
License
Gemma 4 26B A4B
Open Weight
GPT-5.6 Luna
Proprietary
Release date
Gemma 4 26B A4B
2026-04-02
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, 65.6 versus 46.28, but the 90% score intervals overlap.
- 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.
Questions
Which is better, Gemma 4 26B A4B or GPT-5.6 Luna?
GPT-5.6 Luna has the higher public score estimate, 65.6 versus 46.28, but the 90% score intervals overlap. The higher estimate is not a decisive winner because the uncertainty ranges overlap.
Which is better for coding, Gemma 4 26B A4B or GPT-5.6 Luna?
GPT-5.6 Luna leads the public coding lane, 64.5 to 34.3, with Supported evidence for both models and non-overlapping 90% intervals.
Which is better for agentic tasks, Gemma 4 26B A4B or GPT-5.6 Luna?
Gemma 4 26B A4B is not ranked on the public lane for agentic tasks, so no winner is named for agentic tasks.
Which costs less, Gemma 4 26B A4B or GPT-5.6 Luna?
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, Gemma 4 26B A4B or GPT-5.6 Luna?
GPT-5.6 Luna has the larger documented context window: 1.05M, compared with 256K.
Benchmark evidence
The full public result ledger is available for audit without forcing a wide desktop table onto a phone.
Browse raw public benchmark evidence32 rows
Agentic
Terminal-Bench 3.0
Gemma 4 26B A4B—GPT-5.6 Luna14.3%SourceNot directly comparable
Terminal-Bench 2.1
Gemma 4 26B A4B—GPT-5.6 Luna84.7%SourceNot directly comparable
BrowseComp
Gemma 4 26B A4B—GPT-5.6 Luna83.3%SourceNot directly comparable
OSWorld 2.0
Gemma 4 26B A4B—GPT-5.6 Luna45.6%SourceNot directly comparable
CyberGym
Gemma 4 26B A4B—GPT-5.6 Luna77.9%SourceNot directly comparable
ExploitGym
Gemma 4 26B A4B—GPT-5.6 Luna12.4%SourceNot directly comparable
Toolathlon
Gemma 4 26B A4B—GPT-5.6 Luna53.4%SourceNot directly comparable
Terminal-Bench 2.1 (Vals)
Gemma 4 26B A4B—GPT-5.6 Luna79.0%SourceNot directly comparable
ApprenticeBench
Gemma 4 26B A4B—GPT-5.6 Luna7%SourceNot directly comparable
Coding
SWE-bench Pro
Gemma 4 26B A4B—GPT-5.6 Luna62.7%SourceNot directly comparable
Terminal-Bench 2.1
Gemma 4 26B A4B—GPT-5.6 Luna84.7%SourceNot directly comparable
DeepSWE
Gemma 4 26B A4B—GPT-5.6 Luna67.2%SourceNot directly comparable
FrontierCode 1.1 Extended
Gemma 4 26B A4B—GPT-5.6 Luna55.1%SourceNot directly comparable
cursorBench32
Gemma 4 26B A4B—GPT-5.6 Luna61.1%SourceNot directly comparable
VulcanBench v3
Gemma 4 26B A4B—GPT-5.6 Luna85.5%SourceNot directly comparable
SWE-bench (Vals)
Gemma 4 26B A4B—GPT-5.6 Luna93.0%SourceNot directly comparable
Reasoning
Multimodal
Knowledge
MMLU-Pro
Gemma 4 26B A4B82.6%SourceGPT-5.6 Luna—Not directly comparable
HLE
Gemma 4 26B A4B17.2%SourceGPT-5.6 Luna—Not directly comparable
HLE w/o tools
Gemma 4 26B A4B8.7%SourceGPT-5.6 Luna—Not directly comparable
GPQA
Gemma 4 26B A4B—GPT-5.6 Luna92.3%SourceNot directly comparable
GPQA-D
Gemma 4 26B A4B—GPT-5.6 Luna92.3%SourceNot directly comparable
HealthBench Professional
Gemma 4 26B A4B—GPT-5.6 Luna55.7%SourceNot directly comparable
HealthBench Hard
Gemma 4 26B A4B—GPT-5.6 Luna32.0%SourceNot directly comparable
GPQA Diamond (Vals)
Gemma 4 26B A4B—GPT-5.6 Luna91.7%SourceNot directly comparable
MMLU-Pro (Vals)
Gemma 4 26B A4B—GPT-5.6 Luna86.0%SourceNot directly comparable
Math
32 public results · 1 shared
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Last updated September 24, 2026