Side-by-side comparison of pricing, 12 benchmarks, and generation speed.
| Metric | Llama 3.1 Instruct 8B | Gemma 4 E4B (Non-reasoning) |
|---|---|---|
| Input ($/M tokens) | $0.02 | $0.02 |
| Output ($/M tokens) | $0.05 | $0.1 |
Verdict. Llama 3.1 Instruct 8B and Gemma 4 E4B (Non-reasoning) split the benchmark comparison evenly at 1–1. The tiebreaker here is price, speed, and what you actually run them on.
Pricing. Both models sit in the budget bracket for output-token pricing. At 0.5× the per-million-token cost, Llama 3.1 Instruct 8B is meaningfully cheaper if your traffic is output-heavy (long completions, document generation, agent loops). Llama 3.1 Instruct 8B makes more sense when output volume is low and absolute reasoning quality justifies the premium.
Strengths. Llama 3.1 Instruct 8B is strongest on Intelligence Index (7.4), Coding Index (5.4). Gemma 4 E4B (Non-reasoning) leads on Intelligence Index (8.7).
Speed. On throughput, Llama 3.1 Instruct 8B generates tokens at 138 tok/s versus 56 tok/s — about 60% faster. On time-to-first-token, Gemma 4 E4B (Non-reasoning) responds in 700ms vs 930ms, 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 3.1 Instruct 8B costs $1.35 ($16/year); Gemma 4 E4B (Non-reasoning) costs $2.10 ($25/year). At a smaller 5M-input/2M-output scale (single-developer tool or prototype): Llama 3.1 Instruct 8B ≈ $0.20/run, Gemma 4 E4B (Non-reasoning) ≈ $0.30/run. At agent/realtime scale (200M input / 100M output per million requests): Llama 3.1 Instruct 8B ≈ $9/run, Gemma 4 E4B (Non-reasoning) ≈ $14/run. Llama 3.1 Instruct 8B 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
Llama 3.1 Instruct 8B is cheaper overall. Its blended price (3:1 input/output ratio) is $0.03/M tokens vs $0.04/M for Gemma 4 E4B (Non-reasoning).
It's a tie — both models win 1 benchmarks each across 12 evaluated categories. See the detailed benchmark chart above for per-category results.
Llama 3.1 Instruct 8B generates tokens faster at 138 tok/s vs 56 tok/s. However, Gemma 4 E4B (Non-reasoning) has lower time-to-first-token (0.70s vs 0.93s).
Choose based on your priorities: Llama 3.1 Instruct 8B for lower cost, both perform similarly on benchmarks, and Llama 3.1 Instruct 8B for faster generation. For latency-sensitive apps, check the TTFT comparison above.