gpt-oss-120b vs Trinity-Large-Thinking
gpt-oss-120b (2025) and Trinity-Large-Thinking (2026) are frontier reasoning models from OpenAI and Arcee AI. gpt-oss-120b ships a 131k-token context window, while Trinity-Large-Thinking ships a 256k-token context window. On Google-Proof Q&A, Trinity-Large-Thinking leads by 11 pts. On pricing, gpt-oss-120b costs $0.04/1M input tokens versus $0.22/1M for the alternative. This comparison covers specs, pricing, API access, capabilities, benchmarks, input and output token costs, and production fit for coding and agent workloads.
gpt-oss-120b is ~464% cheaper at $0.04/1M; pay for Trinity-Large-Thinking only for reasoning depth.
Decision scorecard
Local evidence first| Signal | gpt-oss-120b | Trinity-Large-Thinking |
|---|---|---|
| Best for | tool-calling agents and provider-routed production | reasoning-heavy apps, tool-calling agents, and provider-routed production |
| Decision fit | RAG, Agents, and Long context | RAG, Agents, and Long context |
| Context window | 131k | 256k |
| Cheapest output | $0.18/1M tokens | $0.85/1M tokens |
| Provider routes | 10 tracked | 3 tracked |
| Shared benchmarks | 1 rows | Google-Proof Q&A leader |
Decision tradeoffs
- gpt-oss-120b has the lower cheapest tracked output price at $0.18/1M tokens.
- gpt-oss-120b has broader tracked provider coverage for fallback and procurement flexibility.
- Local decision data tags gpt-oss-120b for RAG, Agents, and Long context.
- Trinity-Large-Thinking leads the largest shared benchmark signal on Google-Proof Q&A by 11 points.
- Trinity-Large-Thinking has the larger context window for long prompts, retrieval packs, or transcript analysis.
- Trinity-Large-Thinking uniquely exposes Reasoning in local model data.
- Local decision data tags Trinity-Large-Thinking for RAG, Agents, and Long context.
Monthly cost at traffic
Estimate token spend from the cheapest tracked input and output route or tier on this page.
gpt-oss-120b
$76.20
Cheapest tracked route/tier: OpenRouter
Trinity-Large-Thinking
$389
Cheapest tracked route/tier: OpenRouter
Estimated monthly gap: $312. Batch, cache, alternate speed tiers, and negotiated pricing are excluded from this local estimate.
Switch friction
- Provider overlap exists on OpenRouter and Vercel AI Gateway; start route-level A/B tests there.
- Trinity-Large-Thinking is $0.67/1M tokens higher on cheapest tracked output pricing, so quality gains need to justify the spend.
- Trinity-Large-Thinking adds Reasoning in local capability data.
- Provider overlap exists on OpenRouter and Vercel AI Gateway; start route-level A/B tests there.
- gpt-oss-120b is $0.67/1M tokens lower on cheapest tracked output pricing before cache, batch, or negotiated discounts.
- Check replacement coverage for Reasoning before moving production traffic.
Specs
| Specification | ||
|---|---|---|
| Released | 2025-08-05 | 2026-04-01 |
| Context window | 131k | 256k |
| Parameters | 120B | 400B |
| Architecture | decoder only | Sparse Mixture of Experts (MoE) |
| License | Open Source | Apache 2.0 |
| Knowledge cutoff | 2025-08 | - |
Pricing and availability
| Pricing attribute | gpt-oss-120b | Trinity-Large-Thinking |
|---|---|---|
| Input price | $0.04/1M tokens | $0.22/1M tokens |
| Output price | $0.18/1M tokens | $0.85/1M tokens |
| Providers |
Capabilities
| Capability | gpt-oss-120b | Trinity-Large-Thinking |
|---|---|---|
| Vision | No | No |
| Multimodal | No | No |
| Reasoning | No | Yes |
| Function calling | Yes | Yes |
| Tool use | Yes | Yes |
| Structured outputs | Yes | Yes |
| Code execution | No | No |
| IDE integration | No | No |
| Computer use | No | No |
| Parallel agents | No | No |
Benchmarks
| Benchmark | gpt-oss-120b | Trinity-Large-Thinking |
|---|---|---|
| Google-Proof Q&A | 78.2 | 89.2 |
Deep dive
On shared benchmark coverage, Google-Proof Q&A has gpt-oss-120b at 78.2 and Trinity-Large-Thinking at 89.2, with Trinity-Large-Thinking ahead by 11 points. The largest visible gap is 11 points on Google-Proof Q&A, which matters most when that benchmark mirrors your workload. Treat isolated benchmark wins as directional, because provider routing, prompt style, and tool access can move real application results.
The capability footprint differs most on reasoning mode: Trinity-Large-Thinking. Both models share function calling, tool use, and structured outputs, so the practical split is not just feature count. Use those differences to decide whether the page is about raw model quality, agentic coding support, multimodal ingestion, or predictable structured API behavior.
For cost, gpt-oss-120b lists $0.04/1M input and $0.18/1M output tokens on the cheapest tracked provider, while Trinity-Large-Thinking lists $0.22/1M input and $0.85/1M output tokens on the cheapest tracked provider. A 70/30 input-output blend puts gpt-oss-120b lower by about $0.33 per million blended tokens. Availability is 10 providers versus 3, so concentration risk also matters.
Choose gpt-oss-120b when provider fit, lower input-token cost, and broader provider choice are central to the workload. Choose Trinity-Large-Thinking when reasoning depth and larger context windows are more important. For production, rerun your own prompts through the exact provider, region, and tool stack you plan to ship.
FAQ
Which has a larger context window, gpt-oss-120b or Trinity-Large-Thinking?
Trinity-Large-Thinking supports 256k tokens, while gpt-oss-120b supports 131k tokens. That gap matters most for long documents, large codebases, retrieval-heavy agents, and conversations where earlier context must remain visible. Use this as a quick comparison signal, then confirm the provider-specific limits before committing to production.
Which is cheaper, gpt-oss-120b or Trinity-Large-Thinking?
gpt-oss-120b is cheaper on tracked token pricing. gpt-oss-120b costs $0.04/1M input and $0.18/1M output tokens. Trinity-Large-Thinking costs $0.22/1M input and $0.85/1M output tokens. Provider discounts or batch pricing can still change the final bill.
Is gpt-oss-120b or Trinity-Large-Thinking open source?
gpt-oss-120b is listed under Open Source. Trinity-Large-Thinking is listed under Apache 2.0. License labels affect whether you can self-host, redistribute weights, or rely only on hosted APIs, so confirm the upstream license before deployment.
Which is better for reasoning mode, gpt-oss-120b or Trinity-Large-Thinking?
Trinity-Large-Thinking has the clearer documented reasoning mode signal in this comparison. If reasoning mode is mission-critical, validate it against the provider endpoint because model-level support and API-level exposure can differ.
Which is better for function calling, gpt-oss-120b or Trinity-Large-Thinking?
Both gpt-oss-120b and Trinity-Large-Thinking expose function calling. The better choice depends on benchmark fit, context budget, pricing, and whether your provider route exposes the same capability surface. Use this as a quick comparison signal, then confirm the provider-specific limits before committing to production.
Where can I run gpt-oss-120b and Trinity-Large-Thinking?
gpt-oss-120b is available on Cloudflare Workers AI, OpenRouter, Together AI, Fireworks AI, and GCP Vertex AI. Trinity-Large-Thinking is available on Arcee AI, OpenRouter, and Vercel AI Gateway. Provider coverage can affect latency, region availability, compliance posture, and fallback options.
Continue comparing
Last reviewed: 2026-05-22. Data sourced from public model cards and provider documentation.