Side-by-side comparison of pricing, 12 benchmarks, and generation speed.
| Metric | Llama 3.1 Instruct 70B | gpt-oss-120b (low) |
|---|---|---|
| Input ($/M tokens) | $0.56 | $0.15 |
| Output ($/M tokens) | $0.56 | $0.59 |
Verdict. gpt-oss-120b (low) takes the aggregate benchmark matchup 2–0 across 2 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 0.9× the per-million-token cost, Llama 3.1 Instruct 70B is meaningfully cheaper if your traffic is output-heavy (long completions, document generation, agent loops). Llama 3.1 Instruct 70B makes more sense when output volume is low and absolute reasoning quality justifies the premium.
Strengths. Llama 3.1 Instruct 70B is strongest on Intelligence Index (6.5). gpt-oss-120b (low) leads on Coding Index (21.2), Intelligence Index (14.9).
Speed. On throughput, gpt-oss-120b (low) generates tokens at 160 tok/s versus 55 tok/s — about 66% faster. On time-to-first-token, gpt-oss-120b (low) responds in 890ms vs 1270ms, which matters most for chat-style UIs.
Provider. Meta and OpenAI sell to overlapping but distinct developer audiences: Meta tends to ship frontier reasoning models with premium positioning, while OpenAI 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 70B costs $25.20 ($302/year); gpt-oss-120b (low) costs $13.35 ($160/year). At a smaller 5M-input/2M-output scale (single-developer tool or prototype): Llama 3.1 Instruct 70B ≈ $3.92/run, gpt-oss-120b (low) ≈ $1.93/run. At agent/realtime scale (200M input / 100M output per million requests): Llama 3.1 Instruct 70B ≈ $168/run, gpt-oss-120b (low) ≈ $89/run. gpt-oss-120b (low) 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
gpt-oss-120b (low) is cheaper overall. Its blended price (3:1 input/output ratio) is $0.26/M tokens vs $0.56/M for Llama 3.1 Instruct 70B.
gpt-oss-120b (low) wins 2 out of 12 benchmarks compared to 0 for Llama 3.1 Instruct 70B. See the detailed benchmark chart above for per-category results.
gpt-oss-120b (low) generates tokens faster at 160 tok/s vs 55 tok/s. However, gpt-oss-120b (low) has lower time-to-first-token (0.89s vs 1.27s).
Choose based on your priorities: gpt-oss-120b (low) for lower cost, gpt-oss-120b (low) for stronger benchmark performance, and gpt-oss-120b (low) for faster generation. For latency-sensitive apps, check the TTFT comparison above.