Gemma 4 26B A4B vs Llama 4 Scout

Across our 12-test suite, Gemma 4 26B A4B wins the majority (8 wins), Llama 4 Scout wins 1, and 3 tests tie. Test-by-test (score A vs B and rank context):

• Structured output: Gemma 5 vs Scout 4 — Gemma is tied for 1st (tied with 24 others out of 54) for JSON/schema compliance; Scout ranks 26 of 54. This matters when strict format adherence and schema reliability are required.
• Strategic analysis: Gemma 5 vs Scout 2 — Gemma tied for 1st with 25 others (strong at nuanced tradeoff reasoning); Scout ranks 44 of 54, indicating weaker multi-step numeric tradeoffs.
• Creative problem solving: Gemma 4 vs Scout 3 — Gemma ranks 9 of 54 (21 models share score), better for non-obvious, feasible idea generation; Scout is midpack (rank 30).
• Tool calling: Gemma 5 vs Scout 4 — Gemma tied for 1st with 16 others on function selection and argument accuracy; Scout is rank 18 (29 models share). Gemma is more reliable for agent workflows and function sequencing.
• Faithfulness: Gemma 5 vs Scout 4 — Gemma tied for 1st with 32 others out of 55; better when sticking to source material is critical. Scout is midpack (rank 34).
• Persona consistency: Gemma 5 vs Scout 3 — Gemma tied for 1st (36 others), so it better maintains character and resists prompt injection; Scout performs poorly here (rank 45 of 53).
• Agentic planning: Gemma 4 vs Scout 2 — Gemma ranks 16 of 54 (26 models share this score), meaning better at goal decomposition and recovery; Scout ranks 53 of 54.
• Multilingual: Gemma 5 vs Scout 4 — Gemma tied for 1st (34 others); prefer Gemma for non-English parity.
• Safety calibration: Gemma 1 vs Scout 2 — Scout wins here; Gemma ranks 32 of 55 while Scout ranks 12 of 55 (Scout is more likely to refuse harmful prompts appropriately).
• Constrained rewriting: tie 3 vs 3 — both rank 31 of 53; similar when compressing under hard limits.
• Classification: tie 4 vs 4 — both tied for 1st with 29 others (accurate routing/categorization are equal in our tests).
• Long context: tie 5 vs 5 — both tied for 1st with 36 others; both handle retrieval at 30K+ tokens well. Practical takeaway: Gemma repeatedly ranks among the top scorers for structured outputs, tool calling, faithfulness and multilingual outputs — making it preferable where precision and tool-driven workflows matter. Llama 4 Scout’s notable win is safety calibration and a slightly lower output cost; it also offers a larger context window (327,680 vs Gemma 262,144) though both tie at long-context retrieval performance.

Both models share the same input cost $0.08 per mTok; Gemma charges $0.35 per mTok output vs Scout $0.30 per mTok (Gemma is 16.7% more expensive overall per output token). Interpreting mTok as 1,000 tokens: for pure-output workloads at 1M tokens/month (1,000 mTok) Gemma = $350 vs Scout = $300 (difference $50). At 10M tokens/month: Gemma $3,500 vs Scout $3,000 (diff $500). At 100M tokens/month: Gemma $35,000 vs Scout $30,000 (diff $5,000). If workloads are balanced 50% input/50% output, per-month costs for 1M tokens are Gemma $215 vs Scout $190 (diff $25); at 100M tokens the gap is $2,500. Who should care: startups and prototypes can absorb the small absolute gap at low volumes; production services, high-volume APIs, and price-sensitive products should favor Llama 4 Scout to save thousands/month at scale.

Choose Gemma 4 26B A4B if you need: high-fidelity structured output or JSON/schema compliance, reliable tool calling and agentic planning, stronger faithfulness and persona consistency, or top-tier multilingual support. Choose Llama 4 Scout if you need: better safety calibration, a slightly larger context window (327,680 tokens), and lower output cost (output $0.30 vs Gemma $0.35 per mTok) — ideal for cost-sensitive or safety-critical deployments.