OpenAI GPT OSS Safeguard 120B vs Llama 3.1 70B Instruct
OpenAI GPT OSS Safeguard 120B (2025) and Llama 3.1 70B Instruct (2024) are compact production models from OpenAI and AI at Meta. OpenAI GPT OSS Safeguard 120B ships a not-yet-sourced context window, while Llama 3.1 70B Instruct ships a 128K-token context window. On pricing, OpenAI GPT OSS Safeguard 120B costs $0.15/1M input tokens versus $0.4/1M for the alternative. This comparison covers specs, pricing, capabilities, benchmarks, provider availability, and production fit.
OpenAI GPT OSS Safeguard 120B is ~167% cheaper at $0.15/1M; pay for Llama 3.1 70B Instruct only for provider fit.
Decision scorecard
Local evidence first| Signal | OpenAI GPT OSS Safeguard 120B | Llama 3.1 70B Instruct |
|---|---|---|
| Decision fit | Classification and JSON / Tool use | Coding, RAG, and Long context |
| Context window | — | 128K |
| Cheapest output | $0.6/1M tokens | $0.4/1M tokens |
| Provider routes | 1 tracked | 11 tracked |
| Shared benchmarks | 0 rows | 0 rows |
Decision tradeoffs
- Local decision data tags OpenAI GPT OSS Safeguard 120B for Classification and JSON / Tool use.
- Llama 3.1 70B Instruct has the larger context window for long prompts, retrieval packs, or transcript analysis.
- Llama 3.1 70B Instruct has the lower cheapest tracked output price at $0.4/1M tokens.
- Llama 3.1 70B Instruct has broader tracked provider coverage for fallback and procurement flexibility.
- Local decision data tags Llama 3.1 70B Instruct for Coding, RAG, and Long context.
Monthly cost at traffic
Estimate token spend from the cheapest tracked input and output prices on this page.
OpenAI GPT OSS Safeguard 120B
$270
Cheapest tracked route: AWS Bedrock
Llama 3.1 70B Instruct
$420
Cheapest tracked route: Hyperbolic AI Inference
Estimated monthly gap: $150. Batch, cache, and negotiated pricing are excluded from this local estimate.
Switch friction
- Provider overlap exists on AWS Bedrock; start route-level A/B tests there.
- Llama 3.1 70B Instruct is $0.2/1M tokens lower on cheapest tracked output pricing before cache, batch, or negotiated discounts.
- Provider overlap exists on AWS Bedrock; start route-level A/B tests there.
- OpenAI GPT OSS Safeguard 120B is $0.2/1M tokens higher on cheapest tracked output pricing, so quality gains need to justify the spend.
Specs
| Specification | ||
|---|---|---|
| Released | 2025-09-01 | 2024-07-23 |
| Context window | — | 128K |
| Parameters | — | 70B |
| Architecture | - | decoder only |
| License | Proprietary | Open Source |
| Knowledge cutoff | - | - |
Pricing and availability
| Pricing attribute | OpenAI GPT OSS Safeguard 120B | Llama 3.1 70B Instruct |
|---|---|---|
| Input price | $0.15/1M tokens | $0.4/1M tokens |
| Output price | $0.6/1M tokens | $0.4/1M tokens |
| Providers |
Capabilities
| Capability | OpenAI GPT OSS Safeguard 120B | Llama 3.1 70B Instruct |
|---|---|---|
| Vision | No | No |
| Multimodal | No | No |
| Reasoning | No | No |
| Function calling | No | No |
| Tool use | No | No |
| Structured outputs | Yes | Yes |
| Code execution | No | No |
Benchmarks
No shared benchmark rows are currently sourced for this pair.
Deep dive
The capability footprint is close: both models cover structured outputs. That makes context budget, benchmark fit, and provider maturity more important than a simple checklist. If your application depends on one integration detail, verify it against the provider route you plan to use, not just the base model listing.
For cost, OpenAI GPT OSS Safeguard 120B lists $0.15/1M input and $0.6/1M output tokens, while Llama 3.1 70B Instruct lists $0.4/1M input and $0.4/1M output tokens on the cheapest tracked provider. A 70/30 input-output blend puts OpenAI GPT OSS Safeguard 120B lower by about $0.11 per million blended tokens. Availability is 1 providers versus 11, so concentration risk also matters.
Choose OpenAI GPT OSS Safeguard 120B when provider fit and lower input-token cost are central to the workload. Choose Llama 3.1 70B Instruct when provider fit and broader provider choice are more important. For production, rerun your own prompts through the exact provider, region, and tool stack you plan to ship. This keeps the decision grounded in measurable tradeoffs instead of brand-level assumptions. It also helps separate model capability from provider packaging, which can change cost and latency.
FAQ
Which is cheaper, OpenAI GPT OSS Safeguard 120B or Llama 3.1 70B Instruct?
OpenAI GPT OSS Safeguard 120B is cheaper on tracked token pricing. OpenAI GPT OSS Safeguard 120B costs $0.15/1M input and $0.6/1M output tokens. Llama 3.1 70B Instruct costs $0.4/1M input and $0.4/1M output tokens. Provider discounts or batch pricing can still change the final bill.
Is OpenAI GPT OSS Safeguard 120B or Llama 3.1 70B Instruct open source?
OpenAI GPT OSS Safeguard 120B is listed under Proprietary. Llama 3.1 70B Instruct is listed under Open Source. 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 structured outputs, OpenAI GPT OSS Safeguard 120B or Llama 3.1 70B Instruct?
Both OpenAI GPT OSS Safeguard 120B and Llama 3.1 70B Instruct expose structured outputs. The better choice depends on benchmark fit, context budget, pricing, and whether your provider route exposes the same capability surface.
Where can I run OpenAI GPT OSS Safeguard 120B and Llama 3.1 70B Instruct?
OpenAI GPT OSS Safeguard 120B is available on AWS Bedrock. Llama 3.1 70B Instruct is available on OctoAI API (Deprecated), Together AI, Fireworks AI, NVIDIA NIM, and Microsoft Foundry. Provider coverage can affect latency, region availability, compliance posture, and fallback options.
When should I pick OpenAI GPT OSS Safeguard 120B over Llama 3.1 70B Instruct?
OpenAI GPT OSS Safeguard 120B is ~167% cheaper at $0.15/1M; pay for Llama 3.1 70B Instruct only for provider fit. If your workload also depends on provider fit, start with OpenAI GPT OSS Safeguard 120B; if it depends on provider fit, run the same evaluation with Llama 3.1 70B Instruct.
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Last reviewed: 2026-05-16. Data sourced from public model cards and provider documentation.