Gemma 4 E4B vs GPT-5 Nano

At a Glance

Compare
Gemma 4 E4BGoogle DeepMind
Intelligence, Cost, and Efficiency
IntelligenceHigher is better · MM Intelligence v2.5UnrankedNot in the 46-model eligible cohort#46 of 460.0 score · 2/3 sources · provisional · missing LiveBench · full-core range 0.0–33.3
Pricing and Limits
Input priceFrom · USD / 1M tokens$0.020Deepinfra · Sep 21, 2026$0.025Openrouter · Sep 3, 2026
Output priceFrom · USD / 1M tokens$0.10Deepinfra · Sep 21, 2026$0.20Openrouter · Sep 3, 2026
Context windowMaximum documented tokens131K400K
Model facts checkedSep 3, 2026View model evidence →Sep 3, 2026View model evidence →

Token prices are the lowest available sourced USD rates; input and output may use different providers. Cost ranking estimates output spend on LiveBench, not a full request bill. Ranking methodology →

Available Benchmarks

All benchmark results →
No Protocol-Matched Benchmark Yet.Results appear here only when both models share the same benchmark version, metric, evaluation protocol, and evidence class.

Side-by-Side Facts

FieldGemma 4 E4BGPT-5 Nano
DeveloperGoogle DeepMindOpenAI
FamilyGemma 4Gpt 5
ModelGemma 4 E4BGPT-5 Nano
VersionGemma 4 E4BGPT-5 Nano
Lifecycleactiveactive
Released2026-03-022025-08-07
Knowledge cutoffUnknown2024-05-31
Input modalitiesText, Image, Video, AudioText, Image
Output modalitiesTextText
Context window131K400K
Total parametersUnknownUnknown
Active parametersUnknownUnknown
Licenseapache-2.0Unknown
Open weightsYesNo
API availableYesYes
Self-hostableYesNo
Provider accessDeepinfra (Standard), Together Ai (Standard)Openai (Standard), Openrouter (Standard)
Capabilitieschat, generation, reasoning, structured_outputs, toolschat, generation, reasoning, structured_outputs, tools

Gemma 4 E4B Capabilities

chatgenerationreasoningstructured outputstools
Serving providers2
Canonical IDgoogle/gemma-4-E4B-it

GPT-5 Nano Capabilities

chatgenerationreasoningstructured outputstools
Serving providers2
Canonical IDopenai/gpt-5-nano

Primary Evidence

Sources and Freshness

Questions

Gemma 4 E4B vs GPT-5 Nano FAQs

Is Gemma 4 E4B or GPT-5 Nano better for coding?+

This comparison does not currently contain a protocol-matched coding benchmark for both Gemma 4 E4B and GPT-5 Nano, so Model Markets cannot name a coding leader from pricing, context size, or capability labels alone.

Which is cheaper, Gemma 4 E4B or GPT-5 Nano?+

Gemma 4 E4B is $0.020 and GPT-5 Nano is $0.025 per million tokens, so Gemma 4 E4B is cheaper on this metric. Gemma 4 E4B is $0.10 and GPT-5 Nano is $0.20 per million tokens, so Gemma 4 E4B is cheaper on this metric.

Which has a larger context window, Gemma 4 E4B or GPT-5 Nano?+

GPT-5 Nano has the larger sourced context window. Gemma 4 E4B supports 131K and GPT-5 Nano supports 400K.

Which performs better in benchmarks, Gemma 4 E4B or GPT-5 Nano?+

There is no overall benchmark winner: At least two independently verified, protocol-matched benchmarks are required for an overall winner.

Can Gemma 4 E4B or GPT-5 Nano be self-hosted?+

Gemma 4 E4B is the only model in this pair currently marked as self-hostable. Gemma 4 E4B is open weight; GPT-5 Nano is not marked open weight.

Can Gemma 4 E4B and GPT-5 Nano understand images?+

Gemma 4 E4B is documented with image input; GPT-5 Nano is documented with image input. This reflects supported input modalities, not vision quality.

Which can generate longer answers, Gemma 4 E4B or GPT-5 Nano?+

Neither has a larger sourced maximum output. Gemma 4 E4B is — and GPT-5 Nano is 128K.

Do Gemma 4 E4B and GPT-5 Nano support reasoning and tool use?+

Gemma 4 E4B: reasoning, tool calling, and image input. GPT-5 Nano: reasoning, tool calling, and image input. Feature support does not establish relative quality.

Which is available from more inference providers, Gemma 4 E4B or GPT-5 Nano?+

Gemma 4 E4B has 2 sourced provider routes; GPT-5 Nano has 2, a tie.

Which offers better value, Gemma 4 E4B or GPT-5 Nano?+

There is no universal value winner. Compare the input and output prices above with the matched benchmark result for your workload: cheaper tokens can be offset by different quality, token usage, latency, or provider availability.

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