Gemma 4 31B vs Llama 4 Scout

Gemma 4 31B wins 9 of 12 benchmarks in our testing; Llama 4 Scout wins 1, with 2 ties.

Where Gemma 4 31B dominates:

• Agentic planning: 5 vs 2. This is the largest gap in the comparison. Gemma 4 31B ties for 1st with 14 other models out of 54 tested; Scout ranks 53rd of 54 — near the bottom of the entire field. For any workflow requiring goal decomposition, multi-step orchestration, or failure recovery, Scout is not competitive.
• Strategic analysis: 5 vs 2. Gemma 4 31B ties for 1st with 25 others out of 54 tested; Scout ranks 44th of 54. Nuanced tradeoff reasoning with real numbers is a clear Gemma 4 31B strength.
• Persona consistency: 5 vs 3. Gemma 4 31B ties for 1st (37 models at the top); Scout ranks 45th of 53. Character maintenance and injection resistance diverge significantly.
• Tool calling: 5 vs 4. Gemma 4 31B ties for 1st with 16 others out of 54; Scout ranks 18th of 54 with 29 models at the same score. Both are functional, but Gemma 4 31B shows tighter argument accuracy and sequencing in our tests.
• Structured output: 5 vs 4. Gemma 4 31B ties for 1st (25 models) out of 54; Scout ranks 26th. JSON schema compliance is reliable from both, but Gemma 4 31B has an edge.
• Faithfulness: 5 vs 4. Gemma 4 31B ties for 1st (33 models) out of 55; Scout ranks 34th. Hallucination risk is marginally lower with Gemma 4 31B.
• Multilingual: 5 vs 4. Gemma 4 31B ties for 1st (35 models) out of 55; Scout ranks 36th. Equivalent-quality non-English output is a stronger suit for Gemma 4 31B.
• Creative problem solving: 4 vs 3. Gemma 4 31B ranks 9th of 54 (21 models share this score); Scout ranks 30th of 54. Generating non-obvious, feasible ideas skews toward Gemma 4 31B.
• Constrained rewriting: 4 vs 3. Gemma 4 31B ranks 6th of 53; Scout ranks 31st. Compression within hard character limits is another gap.

Where Llama 4 Scout wins:

• Long context: 5 vs 4. Scout ties for 1st with 36 other models out of 55; Gemma 4 31B ranks 38th. Scout's 327K context window (vs Gemma 4 31B's 262K) pairs with its top-tier retrieval accuracy at 30K+ tokens. If your use case centers on processing very long documents, this is Scout's clearest advantage.

Ties:

• Classification: Both score 4, both tied for 1st with 29 other models out of 53. No meaningful difference for routing and categorization tasks.
• Safety calibration: Both score 2, both rank 12th of 55 with 20 models sharing the score. Neither model stands out for balancing refusals vs. legitimate requests — this is a known weak point for both relative to the broader field.

Gemma 4 31B costs $0.13/MTok input and $0.38/MTok output. Llama 4 Scout costs $0.08/MTok input and $0.30/MTok output — 38% cheaper on input and 21% cheaper on output. In absolute terms, the difference is small at low volumes: at 1M output tokens/month, you're paying $380 vs $300 — a $80 gap. At 10M output tokens/month that becomes $800/month, and at 100M tokens/month it's $8,000/month. For throughput-heavy, cost-sensitive applications where the benchmark gaps don't matter — bulk summarization, high-volume classification pipelines — Scout's pricing edge becomes meaningful at scale. But for agentic systems, tool-use pipelines, or anything requiring reliable multi-step planning, the performance gap from Gemma 4 31B's scores justifies the premium. The price ratio is only 1.27x on output, so most developers will find the capability uplift worth it.

Choose Gemma 4 31B if you're building agentic systems, tool-use pipelines, or multi-step reasoning workflows — it scores 5 vs Scout's 2 on agentic planning in our testing, a gap that will materially affect production reliability. It's also the right call for multilingual applications, structured output generation, strategic analysis tasks, and persona-driven chat. The 27% output cost premium over Scout is unlikely to be a deciding factor for most of these use cases.

Choose Llama 4 Scout if your primary use case is long-document processing — it matches the top score (5/5) on long context retrieval and offers a 327K context window. It's also the right pick if you're running very high output volumes (100M+ tokens/month) and the task profile is simple enough that the agentic planning and strategic analysis gaps don't apply — for example, bulk classification or document retrieval where both models tie or Scout leads. At $0.30/MTok output, the savings compound quickly at scale for non-complex tasks.