Gemma 4 26B A4B vs Llama 4 Maverick 17B 128E
At a Glance
| Compare |  Gemma 4 26B A4BGoogle DeepMind | |
|---|---|---|
| Pricing and Limits | ||
| Input priceFrom · USD / 1M tokens | $0.070Deepinfra ↗ · Sep 21, 2026 | Not reported |
| Output priceFrom · USD / 1M tokens | $0.30Openrouter ↗ · Sep 22, 2026 | Not reported |
| Context windowMaximum documented tokens | 262K | 1,000K |
| Model facts checked | Sep 3, 2026View model evidence → | Aug 28, 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
Side-by-Side Facts
| Field | Gemma 4 26B-A4B | Llama-4-Maverick-17B-128E |
|---|---|---|
| Developer | Google DeepMind | Meta |
| Family | Gemma 4 | Llama 4 Maverick 17b 128e |
| Model | Gemma 4 26B-A4B | Llama-4-Maverick-17B-128E |
| Version | Gemma 4 26B-A4B | Llama-4-Maverick-17B-128E |
| Lifecycle | active | active |
| Released | 2026-03-11 | 2025-04-05 |
| Knowledge cutoff | Unknown | Unknown |
| Input modalities | Text, Image | Text, Image |
| Output modalities | Text | Text |
| Context window | 262K | 1,000K |
| Total parameters | 26B | 401.6B |
| Active parameters | 4B | 17B |
| License | apache-2.0 | other |
| Open weights | Yes | Yes |
| API available | Yes | Unknown |
| Self-hostable | Yes | Yes |
| Provider access | Deepinfra (Standard), Google Gemini (Standard), Openrouter (Standard), Together Ai (Standard) | Unknown |
| Capabilities | chat, generation, reasoning, structured_outputs, tools | chat, generation, tools |
Gemma 4 26B A4B Capabilities
Llama 4 Maverick 17B 128E Capabilities
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Primary Evidence
Sources and Freshness
Questions
Gemma 4 26B A4B vs Llama 4 Maverick 17B 128E FAQs
Is Gemma 4 26B A4B or Llama 4 Maverick 17B 128E better for coding?+
This comparison does not currently contain a protocol-matched coding benchmark for both Gemma 4 26B A4B and Llama 4 Maverick 17B 128E, so Model Markets cannot name a coding leader from pricing, context size, or capability labels alone.
Which is cheaper, Gemma 4 26B A4B or Llama 4 Maverick 17B 128E?+
Only Gemma 4 26B A4B has a directly sourced input price: $0.070 per million tokens. Only Gemma 4 26B A4B has a directly sourced output price: $0.30 per million tokens.
Which has a larger context window, Gemma 4 26B A4B or Llama 4 Maverick 17B 128E?+
Llama 4 Maverick 17B 128E has the larger sourced context window. Gemma 4 26B A4B supports 262K and Llama 4 Maverick 17B 128E supports 1,000K.
Which performs better in benchmarks, Gemma 4 26B A4B or Llama 4 Maverick 17B 128E?+
There is no overall benchmark winner: At least two independently verified, protocol-matched benchmarks are required for an overall winner.
Can Gemma 4 26B A4B or Llama 4 Maverick 17B 128E be self-hosted?+
Both models have the same recorded self-hosting status: supported. Gemma 4 26B A4B is open weight; Llama 4 Maverick 17B 128E is open weight.
Can Gemma 4 26B A4B and Llama 4 Maverick 17B 128E understand images?+
Gemma 4 26B A4B is documented with image input; Llama 4 Maverick 17B 128E is documented with image input. This reflects supported input modalities, not vision quality.
Which can generate longer answers, Gemma 4 26B A4B or Llama 4 Maverick 17B 128E?+
Neither has a larger sourced maximum output. Gemma 4 26B A4B is — and Llama 4 Maverick 17B 128E is —.
Do Gemma 4 26B A4B and Llama 4 Maverick 17B 128E support reasoning and tool use?+
Gemma 4 26B A4B: reasoning, tool calling, and image input. Llama 4 Maverick 17B 128E: tool calling and image input. Feature support does not establish relative quality.
Which is available from more inference providers, Gemma 4 26B A4B or Llama 4 Maverick 17B 128E?+
Gemma 4 26B A4B has 4 sourced provider routes; Llama 4 Maverick 17B 128E has 0, so Gemma 4 26B A4B has broader tracked availability.
Which offers better value, Gemma 4 26B A4B or Llama 4 Maverick 17B 128E?+
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.