Side-by-side comparison of pricing, 12 benchmarks, and generation speed.
| Metric | Llama 3.2 Instruct 11B (Vision) | Gemma 4 12B (Non-reasoning) |
|---|---|---|
| Input ($/M tokens) | $0.34 | $0.1 |
| Output ($/M tokens) | $0.34 | $0.3 |
Verdict. Gemma 4 12B (Non-reasoning) takes the aggregate benchmark matchup 1–0 across 1 categories. Real workloads usually care about a handful of specific tasks — see the per-benchmark table above.
Pricing. Both models sit in the budget bracket for output-token pricing. At 1.1× the per-million-token cost, Gemma 4 12B (Non-reasoning) is meaningfully cheaper if your traffic is output-heavy (long completions, document generation, agent loops). Gemma 4 12B (Non-reasoning) makes more sense when output volume is low and absolute reasoning quality justifies the premium.
Strengths. Llama 3.2 Instruct 11B (Vision) is strongest on Intelligence Index (5.4). Gemma 4 12B (Non-reasoning) leads on Intelligence Index (9.4).
Speed. On throughput, Gemma 4 12B (Non-reasoning) generates tokens at 105 tok/s versus 22 tok/s — about 79% faster. On time-to-first-token, Llama 3.2 Instruct 11B (Vision) responds in 1270ms vs 2340ms, which matters most for chat-style UIs.
Provider. Meta and Google sell to overlapping but distinct developer audiences: Meta tends to ship frontier reasoning models with premium positioning, while Google often prices more aggressively. Your existing vendor relationships, billing, and SLA preferences may matter as much as the raw numbers above.
Workload cost. Workload scenarios (per million requests at 30M input + 15M output tokens): Llama 3.2 Instruct 11B (Vision) costs $15.30 ($184/year); Gemma 4 12B (Non-reasoning) costs $7.50 ($90/year). At a smaller 5M-input/2M-output scale (single-developer tool or prototype): Llama 3.2 Instruct 11B (Vision) ≈ $2.38/run, Gemma 4 12B (Non-reasoning) ≈ $1.10/run. At agent/realtime scale (200M input / 100M output per million requests): Llama 3.2 Instruct 11B (Vision) ≈ $102/run, Gemma 4 12B (Non-reasoning) ≈ $50/run. Gemma 4 12B (Non-reasoning) becomes more attractive at higher volume — the absolute per-token pricing difference compounds when you ship at scale.
Recommendation. Both models have legitimate use cases — the right answer depends on whether you are optimizing for benchmark ceiling, latency, or unit cost. Start with the cheaper / faster model, evaluate against your specific task, and only switch if the upgrade shows a meaningful lift.
Head-to-head deltas
Data from Artificial Analysis API — 12 benchmarks
Gemma 4 12B (Non-reasoning) is cheaper overall. Its blended price (3:1 input/output ratio) is $0.15/M tokens vs $0.34/M for Llama 3.2 Instruct 11B (Vision).
Gemma 4 12B (Non-reasoning) wins 1 out of 12 benchmarks compared to 0 for Llama 3.2 Instruct 11B (Vision). See the detailed benchmark chart above for per-category results.
Gemma 4 12B (Non-reasoning) generates tokens faster at 105 tok/s vs 22 tok/s. Llama 3.2 Instruct 11B (Vision) also has lower time-to-first-token (1.27s vs 2.34s).
Choose based on your priorities: Gemma 4 12B (Non-reasoning) for lower cost, Gemma 4 12B (Non-reasoning) for stronger benchmark performance, and Gemma 4 12B (Non-reasoning) for faster generation. For latency-sensitive apps, check the TTFT comparison above.