Gemma 4 26B A4B vs Llama 3.3 70B Instruct

Across our 12-test internal benchmark suite, Gemma 4 26B A4B outperforms Llama 3.3 70B Instruct on 8 tests, ties on 3, and loses on 1.

Where Gemma 4 26B A4B leads:

• Tool calling: 5 vs 4. Gemma 4 26B A4B ties for 1st among 54 models (with 16 others); Llama 3.3 70B Instruct ranks 18th. This matters directly for agentic and function-calling workflows where argument accuracy and sequencing errors are costly.
• Strategic analysis: 5 vs 3. Gemma 4 26B A4B ties for 1st among 54 models (with 25 others); Llama 3.3 70B Instruct ranks 36th of 54. A two-point gap on nuanced tradeoff reasoning is significant for use cases like business analysis, research synthesis, or evaluation tasks.
• Agentic planning: 4 vs 3. Gemma 4 26B A4B ranks 16th of 54; Llama 3.3 70B Instruct ranks 42nd. This covers goal decomposition and failure recovery — critical for multi-step autonomous workflows.
• Multilingual: 5 vs 4. Gemma 4 26B A4B ties for 1st among 55 models (with 34 others); Llama 3.3 70B Instruct ranks 36th. For non-English deployments, this is a meaningful advantage.
• Faithfulness: 5 vs 4. Gemma 4 26B A4B ties for 1st among 55 models (with 32 others); Llama 3.3 70B Instruct ranks 34th. Fewer hallucinations relative to source material.
• Persona consistency: 5 vs 3. Gemma 4 26B A4B ties for 1st among 53 models (with 36 others); Llama 3.3 70B Instruct ranks 45th of 53 — near the bottom. For chatbot and roleplay applications, this gap is substantial.
• Structured output: 5 vs 4. Gemma 4 26B A4B ties for 1st among 54 models (with 24 others); Llama 3.3 70B Instruct ranks 26th. JSON schema compliance and format adherence is better, which matters for any pipeline consuming structured responses.
• Creative problem solving: 4 vs 3. Gemma 4 26B A4B ranks 9th of 54; Llama 3.3 70B Instruct ranks 30th.

Where they tie:

• Classification: both score 4, both tied for 1st among 53 models (with 29 others). Equivalent for routing and categorization tasks.
• Long context: both score 5, both tied for 1st among 55 models (with 36 others). Retrieval accuracy at 30K+ tokens is equivalent — though Gemma 4 26B A4B's 262K context window is twice Llama 3.3 70B Instruct's 131K, which may matter for very large document tasks.
• Constrained rewriting: both score 3, both rank 31st of 53. Neither excels at compression within hard character limits.

Where Llama 3.3 70B Instruct leads:

• Safety calibration: 2 vs 1. Llama 3.3 70B Instruct ranks 12th of 55; Gemma 4 26B A4B ranks 32nd. This is the one area where Llama 3.3 70B Instruct has a clear advantage — it better balances refusing harmful requests while permitting legitimate ones.

External benchmarks (Epoch AI): Llama 3.3 70B Instruct has external benchmark scores available: 41.6% on MATH Level 5 (ranking last of 14 models with that score in our dataset) and 5.1% on AIME 2025 (ranking last of 23 models with that score). These place Llama 3.3 70B Instruct at the lower end of the tracked models on competition math. Gemma 4 26B A4B has no external benchmark scores in the payload for direct comparison on these dimensions.

The two models are priced nearly identically. Gemma 4 26B A4B costs $0.08/M input and $0.35/M output; Llama 3.3 70B Instruct costs $0.10/M input and $0.32/M output — a price ratio of just 1.09x overall. At 1M output tokens/month, the difference is $0.35 vs $0.32 — effectively $0.03. At 10M output tokens, you are looking at $3.50 vs $3.20 — a $0.30 gap. Even at 100M output tokens, the spread is only $3.00. Llama 3.3 70B Instruct is marginally cheaper on output and slightly cheaper on input, but the delta is too small to be a decision driver for virtually any organization. The rare exception would be extreme-scale deployments in the billions of tokens per month where even fractions of a cent per million compound into real dollars — but at that scale, the performance gap in Gemma 4 26B A4B's favor likely justifies the marginal extra cost anyway.

Choose Gemma 4 26B A4B if:

• You are building agentic or tool-calling pipelines — it scores 5 vs 4 on tool calling and 4 vs 3 on agentic planning in our tests.
• Your application involves strategic analysis, business reasoning, or research synthesis — the 5 vs 3 gap is decisive.
• You need reliable structured output (JSON, schemas) — 5 vs 4 reduces format failures in production pipelines.
• Your product serves non-English users — 5 vs 4 on multilingual with a much higher relative ranking.
• You are building a chatbot or persona-driven application — persona consistency is 5 vs 3, with Llama 3.3 70B Instruct ranking 45th of 53 models on that test.
• You need a longer context window — 262K tokens vs 131K is a hard limit difference for large document workloads.

Choose Llama 3.3 70B Instruct if:

• Safety calibration is your primary concern — it scores 2 vs 1 and ranks 12th vs 32nd of 55 models on refusing harmful requests while allowing legitimate ones.
• You have strict output token budget constraints and want marginally lower output cost ($0.32/M vs $0.35/M), and safety calibration is a meaningful factor.
• Your workload is pure text-to-text with no multimodal inputs and you are already integrated into Meta's ecosystem.