Side-by-side comparison of pricing, 12 benchmarks, and generation speed.
| Metric | gpt-oss-120b (high) | Gemini 3 Pro Preview (high) |
|---|---|---|
| Input ($/M tokens) | $0.15 | $2 |
| Output ($/M tokens) | $0.6 | $12 |
Verdict. Gemini 3 Pro Preview (high) takes the aggregate benchmark matchup 10–1 across 11 categories. Real workloads usually care about a handful of specific tasks — see the per-benchmark table above.
Pricing. Both models sit in the budget / mid-tier bracket for output-token pricing. At 0.0× the per-million-token cost, gpt-oss-120b (high) is meaningfully cheaper if your traffic is output-heavy (long completions, document generation, agent loops). gpt-oss-120b (high) makes more sense when output volume is low and absolute reasoning quality justifies the premium.
Strengths. gpt-oss-120b (high) is strongest on AIME 2025 (93%), Math Index (93.4), LiveCodeBench (88%). Gemini 3 Pro Preview (high) leads on Math Index (95.7), AIME 2025 (96%), LiveCodeBench (92%).
Speed. On throughput, gpt-oss-120b (high) generates tokens at 217 tok/s versus 0 tok/s — about 100% faster. On time-to-first-token, Gemini 3 Pro Preview (high) responds in 0ms vs 535ms, which matters most for chat-style UIs.
Provider. OpenAI and Google sell to overlapping but distinct developer audiences: OpenAI 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): gpt-oss-120b (high) costs $13.50 ($162/year); Gemini 3 Pro Preview (high) costs $240.00 ($2880/year). At a smaller 5M-input/2M-output scale (single-developer tool or prototype): gpt-oss-120b (high) ≈ $1.95/run, Gemini 3 Pro Preview (high) ≈ $34.00/run. At agent/realtime scale (200M input / 100M output per million requests): gpt-oss-120b (high) ≈ $90/run, Gemini 3 Pro Preview (high) ≈ $1600/run. gpt-oss-120b (high) 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 (high) is cheaper overall. Its blended price (3:1 input/output ratio) is $0.26/M tokens vs $4.50/M for Gemini 3 Pro Preview (high).
Gemini 3 Pro Preview (high) wins 10 out of 12 benchmarks compared to 1 for gpt-oss-120b (high). See the detailed benchmark chart above for per-category results.
gpt-oss-120b (high) generates tokens faster at 217 tok/s vs 0 tok/s. However, Gemini 3 Pro Preview (high) has lower time-to-first-token (0.00s vs 0.54s).
Choose based on your priorities: gpt-oss-120b (high) for lower cost, Gemini 3 Pro Preview (high) for stronger benchmark performance, and gpt-oss-120b (high) for faster generation. For latency-sensitive apps, check the TTFT comparison above.