Gemini 3 Flash Preview vs Llama 4 Scout

Across our 12-test benchmark suite, Gemini 3 Flash Preview wins 9 categories, Llama 4 Scout wins 1, and they tie on 2.

Where Gemini 3 Flash Preview dominates:

• Agentic planning: 5 vs 2. Flash Preview ties for 1st among 14 other models out of 54 tested; Llama 4 Scout ranks 53rd of 54 — near the bottom of all models we've tested. This is the sharpest gap in the dataset and matters enormously for any workflow involving goal decomposition, multi-step tool use, or failure recovery.
• Strategic analysis: 5 vs 2. Flash Preview ties for 1st with 25 others out of 54; Scout ranks 44th of 54. Nuanced tradeoff reasoning with real numbers is a clear Flash Preview strength.
• Creative problem solving: 5 vs 3. Flash Preview ties for 1st with just 7 other models (a tighter top tier); Scout ranks 30th of 54.
• Tool calling: 5 vs 4. Both pass the basic bar, but Flash Preview ties for 1st with 16 others; Scout ranks 18th of 54. For function selection, argument accuracy, and sequencing in production API integrations, Flash Preview has a measurable edge.
• Faithfulness: 5 vs 4. Flash Preview ties for 1st with 32 others; Scout ranks 34th of 55. Both are solid here, but Flash Preview is more reliable at sticking to source material without hallucinating.
• Persona consistency: 5 vs 3. Flash Preview ties for 1st with 36 others; Scout ranks 45th of 53. A significant gap for chatbot and character-based applications.
• Multilingual: 5 vs 4. Flash Preview ties for 1st with 34 others; Scout ranks 36th of 55. Both pass the median (p50 = 5), but Flash Preview sits at the ceiling.
• Structured output: 5 vs 4. Flash Preview ties for 1st with 24 others; Scout ranks 26th of 54. JSON schema compliance is a tie-broken win for Flash Preview.
• Constrained rewriting: 4 vs 3. Flash Preview ranks 6th of 53; Scout ranks 31st. Compression within hard character limits favors Flash Preview.

Where Llama 4 Scout wins:

• Safety calibration: 2 vs 1. Scout ranks 12th of 55; Flash Preview ranks 32nd of 55. This is Llama 4 Scout's only outright win. Notably, both scores sit below the median (p50 = 2), so neither model excels here — but Scout is meaningfully less miscalibrated than Flash Preview in our testing.

Ties:

• Classification: Both score 4/5, both tied for 1st with 29 other models out of 53.
• Long context: Both score 5/5, both tied for 1st with 36 other models out of 55. At 30K+ token retrieval, they are equivalent.

External benchmarks (Epoch AI): Gemini 3 Flash Preview scores 75.4% on SWE-bench Verified, ranking 3rd of 12 models with that data available — placing it solidly among the top coding models by that external measure. It also scores 92.8% on AIME 2025, ranking 5th of 23. Both scores exceed the dataset medians (p50: 70.8% and 83.9% respectively). Llama 4 Scout has no external benchmark scores in our dataset.

Gemini 3 Flash Preview costs $0.50 per million input tokens and $3.00 per million output tokens. Llama 4 Scout costs $0.08 input and $0.30 output — roughly 6x cheaper on input and 10x cheaper on output. At 1 million output tokens per month, that's $3.00 vs $0.30 — a $2.70 difference that barely registers. At 10 million output tokens, it's $30 vs $3, saving $27/month. At 100 million output tokens — a serious production workload — you're looking at $300 vs $30, a $270/month gap. The cost difference becomes meaningful only at significant scale. Developers running high-volume pipelines where quality requirements are moderate (classification, simple retrieval, lightweight summarization) have a real case for Llama 4 Scout. Anyone building agentic systems, complex multi-step workflows, or applications requiring strong multilingual or reasoning output should weigh the 10x cost premium against what are substantial capability gaps on those specific tasks.

Choose Gemini 3 Flash Preview if you are building agentic workflows, multi-step automation, or anything requiring strong reasoning — its 5/5 on agentic planning (vs Scout's 2/5, near last place) and 5/5 on strategic analysis (vs Scout's 2/5) are not marginal gaps. Also prefer Flash Preview for production tool-calling integrations, multilingual applications, persona-driven chatbots, and coding tasks (75.4% on SWE-bench Verified per Epoch AI). The $0.50/$3.00 pricing is competitive with comparable-quality models in our dataset.

Choose Llama 4 Scout if you are running high-volume, lower-complexity workloads — classification pipelines, long-context retrieval, or simple summarization — where the 4/5 scores match Flash Preview's output at roughly one-tenth the output cost. At 100M+ output tokens/month, the $270/month savings is real. Scout also edges out Flash Preview on safety calibration (2 vs 1), which may matter in consumer-facing applications with strict refusal requirements. Be aware that Scout's 327K context window is smaller than Flash Preview's 1M token window, which could be a constraint for very long document workflows.