Agentic
Directional only- Gemma 4 E4B
- 45.9
- Estimated · #91/151
- GPT-5.5
- 63.9
- Supported · #15/151
- Basis
- BenchAlign lane · 0 vs 13 public rows
- Reading
- Directional only
Gemma 4 E4B vs GPT-5.5
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 42.92, and the 90% score intervals do not overlap.
1 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.
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
Coding work
Code generation, repair, and software-engineering tasks
Not enough matched evidence
Gemma 4 E4B is scored on Estimated evidence for coding, so the reading is directional rather than like-for-like.
Confidence: limited
Agentic work
Tool use, computer use, and multi-step task completion
Not enough matched evidence
Gemma 4 E4B 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
The page does not recommend a cost winner because at least one model cannot fit the stated workload in one request. Gemma 4 E4B does not fit this workload in one request. Gemma 4 E4B has no comparable published API token rate.
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.
1 Gemma 4 E4B only
1 Shared
37 GPT-5.5 only
- Shared results
- 1
- Gemma 4 E4B only
- 1
- GPT-5.5 only
- 37
- Like-for-like categories
- 0 / 8
4 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.
Coding
Directional only- Gemma 4 E4B
- 41.5
- Estimated · #131/183
- GPT-5.5
- 67.7
- Supported · #8/183
- Basis
- BenchAlign lane · 0 vs 9 public rows
- Reading
- Directional only
Knowledge
Directional only- Gemma 4 E4B
- 39.3
- Estimated · #143/181
- GPT-5.5
- 73.3
- Supported · #7/181
- Basis
- BenchAlign lane · 2 vs 6 public rows
- Reading
- Directional only
Instruction following
Directional only- Gemma 4 E4B
- 51.8
- #77/120
- GPT-5.5
- 92.9
- #7/120
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Directional only
Reasoning
Not comparable- Gemma 4 E4B
- 44.7
- Unranked · 2 rankable rows
- GPT-5.5
- 63.5
- #15/22
- Basis
- Provisional lane · 0 vs 2 weighted rows
- Reading
- Not comparable
Math
Not comparable- Gemma 4 E4B
- Not ranked
- GPT-5.5
- 69.6
- Unranked · 3 rankable rows
- Basis
- Provisional lane · 0 vs 2 weighted rows
- Reading
- Not comparable
Multilingual
Not comparable- Gemma 4 E4B
- Not ranked
- GPT-5.5
- Not ranked
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Not comparable
Multimodal
Not comparable- Gemma 4 E4B
- 35.8
- Unranked · 1 rankable row
- GPT-5.5
- 71.3
- #19/48
- Basis
- Provisional lane · 0 vs 2 weighted rows
- Reading
- Not comparable
| Category | Gemma 4 E4B | GPT-5.5 | Basis | Reading |
|---|---|---|---|---|
| Agentic | 45.9Estimated · #91/151 | 63.9Supported · #15/151 | Directional onlyBenchAlign lane · 0 vs 13 public rows | Directional only |
| Coding | 41.5Estimated · #131/183 | 67.7Supported · #8/183 | Directional onlyBenchAlign lane · 0 vs 9 public rows | Directional only |
| Knowledge | 39.3Estimated · #143/181 | 73.3Supported · #7/181 | Directional onlyBenchAlign lane · 2 vs 6 public rows | Directional only |
| Instruction following | 51.8#77/120 | 92.9#7/120 | Directional onlyProvisional lane · 0 vs 0 weighted rows | Directional only |
| Reasoning | 44.7Unranked · 2 rankable rows | 63.5#15/22 | Not comparableProvisional lane · 0 vs 2 weighted rows | Not comparable |
| Math | Not ranked | 69.6Unranked · 3 rankable rows | 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 |
| Multimodal | 35.8Unranked · 1 rankable row | 71.3#19/48 | 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) 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.
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
- Gemma 4 E4B
- Self-hosted; infrastructure cost varies
- Fits in one request
- GPT-5.5
- $0.02
- Fits in one request
Gemma 4 E4B has no comparable published API token rate.
Repository review
50K fresh input + 3K output tokens
- Gemma 4 E4B
- Self-hosted; infrastructure cost varies
- Fits in one request
- GPT-5.5
- $0.34
- Fits in one request
Gemma 4 E4B has no comparable published API token rate.
Cache-heavy agent loop
200K cached + 20K fresh input + 10K output tokens
- Gemma 4 E4B
- Self-hosted; infrastructure cost varies
- Does not fit in one request
- Cached-input rate unavailable
- GPT-5.5
- $0.5
- Fits in one request
Gemma 4 E4B does not fit this workload in one request. Gemma 4 E4B 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.
SpecificationGemma 4 E4BGPT-5.5
Context window
Maximum documented context; output-token limits may be lower.
Gemma 4 E4B
GPT-5.5
API model ID
Gemma 4 E4B
Not sourced
GPT-5.5
gpt-5.5
OpenAI pricingCached-input rate
A missing cached-input rate falls back to the listed input rate only in the stated workload estimate.
Gemma 4 E4B
No comparable hosted API rate
GPT-5.5
$0.5 per 1M cached input tokens
OpenAI pricingDocumented inputs
Gemma 4 E4B
text, image, audio, video
Google Gemma 4 model documentationGPT-5.5
Not sourced
Documented outputs
Gemma 4 E4B
GPT-5.5
Not sourced
Provider availability
Gemma 4 E4B
Open Weights · open weights
Google gemma-4-E4B model cardGPT-5.5
Not sourced
Reasoning profile
Gemma 4 E4B
Reasoning
GPT-5.5
Reasoning
Weight access
Gemma 4 E4B
Open Weight
GPT-5.5
Proprietary
License
Gemma 4 E4B
Open Weight
GPT-5.5
Proprietary
Release date
Gemma 4 E4B
2026-04-02
GPT-5.5
2026-04-23
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 42.92, 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 evidence39 rows
Agentic
Terminal-Bench 2.0
Gemma 4 E4B—GPT-5.582%SourceNot directly comparable
CyberGym
Gemma 4 E4B—GPT-5.581.8%SourceNot directly comparable
BrowseComp
Gemma 4 E4B—GPT-5.584.4%SourceNot directly comparable
OSWorld-Verified
Gemma 4 E4B—GPT-5.578.7%SourceNot directly comparable
MCP Atlas
Gemma 4 E4B—GPT-5.575.3%SourceNot directly comparable
Toolathlon
Gemma 4 E4B—GPT-5.555.6%SourceNot directly comparable
τ²-bench results
Gemma 4 E4B—GPT-5.598%SourceNot directly comparable
Gert Labs
Gemma 4 E4B—GPT-5.572.93%SourceNot directly comparable
ResearchClawBench
Gemma 4 E4B—GPT-5.517.0%SourceNot directly comparable
OSWorld 2.0
Gemma 4 E4B—GPT-5.513.0%SourceNot directly comparable
JobBench
Gemma 4 E4B—GPT-5.542.7%SourceNot directly comparable
ExploitGym
Gemma 4 E4B—GPT-5.513.4%SourceNot directly comparable
Terminal-Bench 2.1 (Vals)
Gemma 4 E4B—GPT-5.576.4%SourceNot directly comparable
Coding
SWE-bench Pro
Gemma 4 E4B—GPT-5.558.6%SourceNot directly comparable
Terminal-Bench 2.0
Gemma 4 E4B—GPT-5.582.0%SourceNot directly comparable
Vibe Code Bench
Gemma 4 E4B—GPT-5.569.85%SourceNot directly comparable
React Native Evals
Gemma 4 E4B—GPT-5.584.7%SourceNot directly comparable
cursorBench31
Gemma 4 E4B—GPT-5.559.2%SourceNot directly comparable
cursorBench32
Gemma 4 E4B—GPT-5.558.4%SourceNot directly comparable
FrontierCode 1.1 Main
Gemma 4 E4B—GPT-5.543.0%SourceNot directly comparable
LiveCodeBench (Vals)
Gemma 4 E4B—GPT-5.585.3%SourceNot directly comparable
SWE-bench (Vals)
Gemma 4 E4B—GPT-5.582.6%SourceNot directly comparable
Reasoning
Knowledge
GPQA
Gemma 4 E4B58.6%SourceGPT-5.593.6%SourceGPT-5.5 leads this result
MMLU-Pro
Gemma 4 E4B69.4%SourceGPT-5.5—Not directly comparable
GPQA-D
Gemma 4 E4B—GPT-5.593.6%SourceNot directly comparable
HLE
Gemma 4 E4B—GPT-5.552.2%SourceNot directly comparable
HLE w/o tools
Gemma 4 E4B—GPT-5.541.4%SourceNot directly comparable
GPQA Diamond (Vals)
Gemma 4 E4B—GPT-5.593.2%SourceNot directly comparable
MMLU-Pro (Vals)
Gemma 4 E4B—GPT-5.588.1%SourceNot directly comparable
Math
Multimodal
Frequently asked questions
Which is better, Gemma 4 E4B or GPT-5.5?
GPT-5.5 has the higher public score, 73.27 versus 42.92, 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 E4B or GPT-5.5?
GPT-5.5 scores higher for coding on the public lane, 67.7 to 41.5. Gemma 4 E4B 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 E4B or GPT-5.5?
GPT-5.5 scores higher for agentic tasks on the public lane, 63.9 to 45.9. Gemma 4 E4B 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, Gemma 4 E4B or GPT-5.5?
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 E4B or GPT-5.5?
GPT-5.5 has the larger documented context window: 1M, compared with 128K.
Related comparisons
Last updated September 4, 2026
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