Gemma 4 31B vs Llama 3.3 70B Instruct

Gemma 4 31B wins 9 of 12 benchmarks in our testing, ties 2, and loses 1. Here is the test-by-test breakdown:

Gemma 4 31B wins:

• Tool calling: 5 vs 4. Gemma 4 31B is tied for 1st among 17 models (out of 54 tested); Llama 3.3 70B Instruct ranks 18th. For agentic pipelines that require accurate function selection and argument passing, this is a meaningful gap.
• Agentic planning: 5 vs 3. Gemma 4 31B is tied for 1st among 15 models; Llama 3.3 70B Instruct ranks 42nd of 54. Goal decomposition and failure recovery are substantially weaker in Llama 3.3 70B Instruct on our tests — a significant concern for multi-step automation.
• Strategic analysis: 5 vs 3. Gemma 4 31B is tied for 1st among 26 models; Llama 3.3 70B Instruct ranks 36th of 54. This covers nuanced tradeoff reasoning with real numbers — relevant to business analysis, planning documents, and advisory tasks.
• Structured output: 5 vs 4. Gemma 4 31B is tied for 1st among 25 models; Llama 3.3 70B Instruct ranks 26th of 54. JSON schema compliance and format adherence matter for any API integration or data extraction pipeline.
• Faithfulness: 5 vs 4. Gemma 4 31B is tied for 1st among 33 models; Llama 3.3 70B Instruct ranks 34th of 55. Sticking to source material without hallucinating is critical for RAG and summarization applications.
• Persona consistency: 5 vs 3. Gemma 4 31B is tied for 1st among 37 models; Llama 3.3 70B Instruct ranks 45th of 53. For chatbot and roleplay deployments, maintaining character and resisting prompt injection is considerably stronger in Gemma 4 31B.
• Multilingual: 5 vs 4. Gemma 4 31B is tied for 1st among 35 models; Llama 3.3 70B Instruct ranks 36th of 55.
• Creative problem solving: 4 vs 3. Gemma 4 31B ranks 9th of 54; Llama 3.3 70B Instruct ranks 30th of 54.
• Constrained rewriting: 4 vs 3. Gemma 4 31B ranks 6th of 53; Llama 3.3 70B Instruct ranks 31st of 53.

Ties:

• Classification: Both score 4/5, both tied for 1st among 30 models out of 53 tested.
• Safety calibration: Both score 2/5, both rank 12th of 55. Neither model distinguishes itself here — both sit below the field median (p50 = 2, p75 = 2), meaning this is a shared limitation.

Llama 3.3 70B Instruct wins:

• Long context: 5 vs 4. Llama 3.3 70B Instruct is tied for 1st among 37 models; Gemma 4 31B ranks 38th of 55. Paradoxically, Gemma 4 31B has a 256K token context window vs Llama 3.3 70B Instruct's 128K, yet Llama 3.3 70B Instruct scored higher on our 30K+ token retrieval accuracy test. The context window size does not guarantee retrieval performance.

External benchmarks (Epoch AI): Llama 3.3 70B Instruct has third-party scores available: 41.6% on MATH Level 5 (ranks 14th of 14 models with this score in the dataset — last place among scored models) and 5.1% on AIME 2025 (ranks 23rd of 23). These scores are well below the dataset medians (p50: 94.15% on MATH Level 5, 83.9% on AIME 2025), indicating Llama 3.3 70B Instruct is not competitive on advanced competition mathematics. Gemma 4 31B has no external benchmark scores in our dataset, so no direct comparison can be made on this dimension.

Gemma 4 31B costs $0.13/MTok input and $0.38/MTok output. Llama 3.3 70B Instruct costs $0.10/MTok input and $0.32/MTok output. The overall price ratio is roughly 1.19x in favor of Llama 3.3 70B Instruct. In practice, the gap is small but grows with output-heavy workloads:

• At 1M output tokens/month: Gemma 4 31B costs $0.38 vs $0.32 — a $0.06 difference, negligible for almost any team.
• At 10M output tokens/month: $3.80 vs $3.20 — a $0.60 gap, still low overhead.
• At 100M output tokens/month: $38 vs $32 — a $6 difference. At this scale, cost-sensitive commodity workloads (bulk classification, simple summarization) may lean toward Llama 3.3 70B Instruct, but for anything requiring agentic or structured output quality, the benchmark gap likely outweighs the saving.

For developers running high-volume pipelines where Gemma 4 31B's benchmark advantages don't apply to the task, Llama 3.3 70B Instruct's lower price is a reasonable reason to choose it. For most API use cases under 10M tokens/month, the cost difference is not a meaningful factor.

Choose Gemma 4 31B if:

• You are building agentic or tool-using systems — it scores 5/5 on both tool calling and agentic planning in our tests, vs Llama 3.3 70B Instruct's 4 and 3 respectively.
• Your application involves structured output (JSON schemas, API integrations): Gemma 4 31B scores 5 vs 4.
• You need strong strategic analysis, constrained writing, or multilingual output quality.
• You need image or video input alongside text — Gemma 4 31B supports text+image+video input; Llama 3.3 70B Instruct is text-only.
• You want a 256K context window (vs 128K on Llama 3.3 70B Instruct), even though long-context retrieval performance in our tests currently favors Llama 3.3 70B Instruct.
• You are deploying a chatbot or persona-driven product: persona consistency scores 5 vs 3.

Choose Llama 3.3 70B Instruct if:

• Long-context retrieval accuracy is your primary concern and you need the best score on that specific benchmark (5 vs 4 in our testing).
• You are running very high output volumes (100M+ tokens/month) on simple tasks where Gemma 4 31B's quality advantages do not apply, and the $0.06/MTok output cost saving is material.
• You need logprobs or top_logprobs support — these parameters are listed for Llama 3.3 70B Instruct but not for Gemma 4 31B in the payload.
• Your task is text-only and you have no need for multimodal input.