Long documents
Prompts that approach the documented context limit
GPT-5.6 Sol
GPT-5.6 Sol has the larger documented context window.
Confidence: documented
Gemma 4 12B vs GPT-5.6 Sol
Updated September 24, 2026. Rank says GPT-5.6 Sol 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
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Decision reading
GPT-5.6 Sol has the higher public score, 78.49 versus 28.47, and the 90% score intervals do not overlap. 3 results are shared. Category rows resting on Estimated evidence or different benchmark sets are marked directional and do not name a winner.
- Shared results
- 3
- Gemma 4 12B only
- 9
- GPT-5.6 Sol only
- 34
- Like-for-like categories
- 2 / 8
Estimated: Gemma 4 12B · Supported: GPT-5.6 SolHow 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.
Show secondary and unsupported calls
Coding work
Code generation, repair, and software-engineering tasks
Not enough matched evidence
Gemma 4 12B is scored on Estimated evidence for coding, so the reading is directional rather than like-for-like.
Confidence: limitedAgentic work
Tool use, computer use, and multi-step task completion
Not enough matched evidence
Gemma 4 12B 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.
22.0Gemma 4 12B71.6GPT-5.6 Sol
Directional only · BenchAlign v5.7
GPT-5.6 Sol scores higher, but at least one score rests on Estimated evidence or a different benchmark set. Directional only, no winner.
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.
9 Gemma 4 12B only
3 Shared
34 GPT-5.6 Sol only
2 categories rest on Estimated evidence or different evidence sets. Those rows remain visible for coverage context but do not name a winner.
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.
GPQAKnowledge
Normalized gap 15.8Gemma 4 12B:78.8%GPT-5.6 Sol:94.6%MMMU-ProMultimodal
Normalized gap 13.9Gemma 4 12B:69.1%GPT-5.6 Sol:83%
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.
Multimodal
Like-for-like- Gemma 4 12B
- 26.2
- #46/50
- GPT-5.6 Sol
- 87.6
- #5/50
- Basis
- Provisional lane · 1 vs 1 weighted rows
- Reading
- GPT-5.6 Sol leads
Knowledge
Like-for-like- Gemma 4 12B
- 31.1
- Supported · #125/158
- GPT-5.6 Sol
- 78.8
- Supported · #7/158
- Basis
- BenchAlign v5.7 lane · 5 vs 8 public rows
- Reading
- GPT-5.6 Sol leads
Coding
Directional only- Gemma 4 12B
- 22.0
- Estimated · #110/135
- GPT-5.6 Sol
- 71.6
- Supported · #6/135
- Basis
- BenchAlign v5.7 lane · 1 vs 12 public rows
- Reading
- Directional only
Instruction following
Directional only- Gemma 4 12B
- 88.7
- #22/124
- GPT-5.6 Sol
- 87.7
- #28/124
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Directional only
Agentic
Not comparable- Gemma 4 12B
- Not ranked
- GPT-5.6 Sol
- 69.6
- Supported · #7/105
- Basis
- BenchAlign v5.7 lane · 0 vs 9 public rows
- Reading
- Not comparable
Reasoning
Not comparable- Gemma 4 12B
- 34.7
- Unranked · 4 rankable rows
- GPT-5.6 Sol
- 72.1
- #8/19
- Basis
- Provisional lane · 1 vs 2 weighted rows
- Reading
- Not comparable
Multilingual
Not comparable- Gemma 4 12B
- Not ranked
- GPT-5.6 Sol
- Not ranked
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Not comparable
Math
Not comparable- Gemma 4 12B
- 53.0
- Unranked · 1 rankable row
- GPT-5.6 Sol
- 96.8
- Unranked · 3 rankable rows
- Basis
- Provisional lane · 1 vs 2 weighted rows
- Reading
- Not comparable
| Category | Gemma 4 12B | GPT-5.6 Sol | Basis | Reading |
|---|---|---|---|---|
| Multimodal | 26.2#46/50 | 87.6#5/50 | Like-for-likeProvisional lane · 1 vs 1 weighted rows | GPT-5.6 Sol leads |
| Knowledge | 31.1Supported · #125/158 | 78.8Supported · #7/158 | Like-for-likeBenchAlign v5.7 lane · 5 vs 8 public rows | GPT-5.6 Sol leads |
| Coding | 22.0Estimated · #110/135 | 71.6Supported · #6/135 | Directional onlyBenchAlign v5.7 lane · 1 vs 12 public rows | Directional only |
| Instruction following | 88.7#22/124 | 87.7#28/124 | Directional onlyProvisional lane · 0 vs 0 weighted rows | Directional only |
| Agentic | Not ranked | 69.6Supported · #7/105 | Not comparableBenchAlign v5.7 lane · 0 vs 9 public rows | Not comparable |
| Reasoning | 34.7Unranked · 4 rankable rows | 72.1#8/19 | Not comparableProvisional lane · 1 vs 2 weighted rows | Not comparable |
| Multilingual | Not ranked | Not ranked | Not comparableProvisional lane · 0 vs 0 weighted rows | Not comparable |
| Math | 53.0Unranked · 1 rankable row | 96.8Unranked · 3 rankable rows | Not comparableProvisional lane · 1 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 12B
- API rate not published
- Fits in one request
- GPT-5.6 Sol
- $0.014
- Fits in one request
Gemma 4 12B has no comparable published API token rate.
Repository review
50K fresh input + 3K output tokens
- Gemma 4 12B
- API rate not published
- Fits in one request
- GPT-5.6 Sol
- $0.26
- Fits in one request
Gemma 4 12B has no comparable published API token rate.
Cache-heavy agent loop
200K cached + 20K fresh input + 10K output tokens
- Gemma 4 12B
- API rate not published
- Fits in one request
- Cached-input rate unavailable
- GPT-5.6 Sol
- $0.36
- Fits in one request
Gemma 4 12B 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 12BGPT-5.6 Sol
Context window
Maximum documented context; output-token limits may be lower.
Gemma 4 12B
GPT-5.6 Sol
1.05M
OpenAI model catalogAPI model ID
Gemma 4 12B
Not sourced
GPT-5.6 Sol
gpt-5.6-sol
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 12B
No comparable hosted API rate
GPT-5.6 Sol
$0.4 per 1M cached input tokens
OpenAI pricingDocumented inputs
Gemma 4 12B
text, image, audio, video
Google Gemma 4 model documentationGPT-5.6 Sol
text, image
OpenAI model catalogDocumented outputs
Gemma 4 12B
GPT-5.6 Sol
Provider availability
Gemma 4 12B
Open Weights · open weights
Google gemma-4-12B model cardGPT-5.6 Sol
Generally Available · OpenAI Responses API
OpenAI model catalogReasoning profile
Gemma 4 12B
Reasoning
GPT-5.6 Sol
Reasoning
Weight access
Gemma 4 12B
Open Weight
GPT-5.6 Sol
Proprietary
License
Gemma 4 12B
Open Weight
GPT-5.6 Sol
Proprietary
Release date
Gemma 4 12B
2026-06-03
GPT-5.6 Sol
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 Sol has the higher public score, 78.49 versus 28.47, 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-5.6 Sol 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 12B or GPT-5.6 Sol?
GPT-5.6 Sol has the higher public score, 78.49 versus 28.47, 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, Gemma 4 12B or GPT-5.6 Sol?
GPT-5.6 Sol scores higher for coding on the public lane, 71.6 to 22. Gemma 4 12B is scored on Estimated evidence for coding, so the reading is directional rather than like-for-like. BenchLM does not name a winner for coding from a directional reading; read the shared benchmark rows directly and test the models on the same task set.
Which is better for agentic tasks, Gemma 4 12B or GPT-5.6 Sol?
Gemma 4 12B is not ranked on the public lane for agentic tasks, so no winner is named for agentic tasks.
Which costs less, Gemma 4 12B or GPT-5.6 Sol?
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 12B or GPT-5.6 Sol?
GPT-5.6 Sol 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 evidence46 rows
Agentic
Terminal-Bench 3.0
Gemma 4 12B—GPT-5.6 Sol34.6%SourceNot directly comparable
Terminal-Bench 2.1
Gemma 4 12B—GPT-5.6 Sol91.9%SourceNot directly comparable
BrowseComp
Gemma 4 12B—GPT-5.6 Sol92.2%SourceNot directly comparable
OSWorld 2.0
Gemma 4 12B—GPT-5.6 Sol62.6%SourceNot directly comparable
CyberGym
Gemma 4 12B—GPT-5.6 Sol84.5%SourceNot directly comparable
ExploitGym
Gemma 4 12B—GPT-5.6 Sol33.7%SourceNot directly comparable
Toolathlon
Gemma 4 12B—GPT-5.6 Sol58%SourceNot directly comparable
Terminal-Bench 2.1 (Vals)
Gemma 4 12B—GPT-5.6 Sol85.8%SourceNot directly comparable
ApprenticeBench
Gemma 4 12B—GPT-5.6 Sol26%SourceNot directly comparable
Coding
LiveCodeBench v6
Gemma 4 12B72.0%SourceGPT-5.6 Sol—Not directly comparable
Bug Hunt Bench
Gemma 4 12B—GPT-5.6 Sol42 fixesSourceNot directly comparable
SWE-bench Pro
Gemma 4 12B—GPT-5.6 Sol64.6%SourceNot directly comparable
Terminal-Bench 2.1
Gemma 4 12B—GPT-5.6 Sol91.9%SourceNot directly comparable
DeepSWE
Gemma 4 12B—GPT-5.6 Sol72.7%SourceNot directly comparable
FrontierCode 1.1 Extended
Gemma 4 12B—GPT-5.6 Sol60.6%SourceNot directly comparable
FrontierSWE v2
Gemma 4 12B—GPT-5.6 Sol32.2%SourceNot directly comparable
cursorBench32
Gemma 4 12B—GPT-5.6 Sol67.2%SourceNot directly comparable
VulcanBench v3
Gemma 4 12B—GPT-5.6 Sol87.0%SourceNot directly comparable
VulcanBench CII v1
Gemma 4 12B—GPT-5.6 Sol86.5%SourceNot directly comparable
LiveCodeBench (Vals)
Gemma 4 12B—GPT-5.6 Sol82.6%SourceNot directly comparable
SWE-bench (Vals)
Gemma 4 12B—GPT-5.6 Sol96.2%SourceNot directly comparable
cursorBench40
Gemma 4 12B—GPT-5.6 Sol41.7%SourceNot directly comparable
Reasoning
BBH
Gemma 4 12B53%SourceGPT-5.6 Sol—Not directly comparable
MRCRv2
Gemma 4 12B43.4%SourceGPT-5.6 Sol—Not directly comparable
ARC-AGI-2
Gemma 4 12B—GPT-5.6 Sol92.5%SourceNot directly comparable
ARC-AGI-3
Gemma 4 12B—GPT-5.6 Sol7.8%SourceNot directly comparable
GeneBench-Pro
Gemma 4 12B—GPT-5.6 Sol28.7%SourceNot directly comparable
Multimodal
Knowledge
GPQA
Gemma 4 12B78.8%SourceGPT-5.6 Sol94.6%SourceGPT-5.6 Sol leads this result
GPQA-D
Gemma 4 12B78.8%SourceGPT-5.6 Sol94.6%SourceGPT-5.6 Sol leads this result
MMLU-Pro
Gemma 4 12B77.2%SourceGPT-5.6 Sol—Not directly comparable
HLE w/o tools
Gemma 4 12B5.2%SourceGPT-5.6 Sol—Not directly comparable
MMMLU
Gemma 4 12B83.4%SourceGPT-5.6 Sol—Not directly comparable
HLE-Verified
Gemma 4 12B—GPT-5.6 Sol54.5%SourceNot directly comparable
LABBench2
Gemma 4 12B—GPT-5.6 Sol82.1%SourceNot directly comparable
HealthBench Professional
Gemma 4 12B—GPT-5.6 Sol60.5%SourceNot directly comparable
HealthBench Hard
Gemma 4 12B—GPT-5.6 Sol33.1%SourceNot directly comparable
GPQA Diamond (Vals)
Gemma 4 12B—GPT-5.6 Sol95.2%SourceNot directly comparable
MMLU-Pro (Vals)
Gemma 4 12B—GPT-5.6 Sol89.1%SourceNot directly comparable
Math
AIME26
Gemma 4 12B77.5%SourceGPT-5.6 Sol—Not directly comparable
FrontierMath (legacy)
Gemma 4 12B—GPT-5.6 Sol89%SourceNot directly comparable
FrontierMath v2 (Tiers 1-3)
Gemma 4 12B—GPT-5.6 Sol89.000%SourceNot directly comparable
FrontierMath v2 (Tier 4)
Gemma 4 12B—GPT-5.6 Sol83.000%SourceNot directly comparable
46 public results · 3 shared
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Last updated September 24, 2026