Llama 2 70B Chat vs text-davinci
Llama 2 70B Chat (2023) and text-davinci (2022) are compact production models from AI at Meta and OpenAI. Llama 2 70B Chat ships a 4k-token context window, while text-davinci ships a 4k-token context window. This comparison covers specs, pricing, API access, capabilities, benchmarks, input and output token costs, and production fit for coding and agent workloads. It focuses on practical selection signals rather than broad model-family marketing.
Llama 2 70B Chat is safer overall; choose text-davinci when provider fit matters.
Decision scorecard
Local evidence first| Signal | Llama 2 70B Chat | text-davinci |
|---|---|---|
| Best for | provider-routed production | general production evaluation |
| Decision fit | Classification and JSON / Tool use | General |
| Context window | 4k | 4k |
| Cheapest output | $1.50/1M tokens | - |
| Provider routes | 14 tracked | 0 tracked |
| Shared benchmarks | 0 rows | 0 rows |
Decision tradeoffs
- Llama 2 70B Chat has broader tracked provider coverage for fallback and procurement flexibility.
- Llama 2 70B Chat uniquely exposes Structured outputs in local model data.
- Local decision data tags Llama 2 70B Chat for Classification and JSON / Tool use.
- Use text-davinci when your own prompt tests beat the comparison signals; the local data does not show a decisive standalone advantage yet.
Monthly cost at traffic
Estimate token spend from the cheapest tracked input and output route or tier on this page.
Llama 2 70B Chat
$775
Cheapest tracked route/tier: Databricks Foundation Model Serving
text-davinci
Unavailable
No complete token price in local provider data
Cost delta unavailable until both models have sourced input and output token prices.
Switch friction
- No overlapping tracked provider route is sourced for Llama 2 70B Chat and text-davinci; plan for SDK, billing, or endpoint changes.
- Check replacement coverage for Structured outputs before moving production traffic.
- No overlapping tracked provider route is sourced for text-davinci and Llama 2 70B Chat; plan for SDK, billing, or endpoint changes.
- Llama 2 70B Chat adds Structured outputs in local capability data.
Specs
| Specification | ||
|---|---|---|
| Released | 2023-07-18 | 2022-01-27 |
| Context window | 4k | 4k |
| Parameters | 70B | 175B |
| Architecture | decoder only | decoder only |
| License | Llama 2 Community | Proprietary |
| Openness | Open weights | Proprietary |
| Commercial use | Commercial use with conditions | Commercial use with conditions |
| Knowledge cutoff | - | 2021-06 |
Pricing and availability
| Pricing attribute | Llama 2 70B Chat | text-davinci |
|---|---|---|
| Input price | $0.50/1M tokens | - |
| Output price | $1.50/1M tokens | - |
| Providers | - |
Capabilities
| Capability | Llama 2 70B Chat | text-davinci |
|---|---|---|
| Vision | No | No |
| Multimodal | No | No |
| Reasoning | No | No |
| Function calling | No | No |
| Tool use | No | No |
| Structured outputs | Yes | No |
| Code execution | No | No |
| IDE integration | No | No |
| Computer use | No | No |
| Parallel agents | No | No |
Benchmarks
No shared benchmark rows are currently sourced for this pair.
Deep dive
The capability footprint differs most on structured outputs: Llama 2 70B Chat. Both models share the core language-model surface, 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.
Pricing coverage is uneven: Llama 2 70B Chat has $0.50/1M input tokens and text-davinci has no token price sourced yet. Provider availability is 14 tracked routes versus 0. Treat unknown pricing as an integration gap, then verify the route you will actually call before estimating production spend.
Choose Llama 2 70B Chat when provider fit and broader provider choice are central to the workload. Choose text-davinci when provider fit 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. For teams standardizing a stack, that distinction is often the difference between a benchmark win and a reliable deployment.
FAQ
Which has a larger context window, Llama 2 70B Chat or text-davinci?
Llama 2 70B Chat supports 4k tokens, while text-davinci supports 4k tokens. That gap matters most for long documents, large codebases, retrieval-heavy agents, and conversations where earlier context must remain visible.
Is Llama 2 70B Chat or text-davinci open source?
Llama 2 70B Chat is listed under Llama 2 Community. text-davinci is listed under Proprietary. 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, Llama 2 70B Chat or text-davinci?
Llama 2 70B Chat has the clearer documented structured outputs signal in this comparison. If structured outputs is mission-critical, validate it against the provider endpoint because model-level support and API-level exposure can differ.
Where can I run Llama 2 70B Chat and text-davinci?
Llama 2 70B Chat is available on Databricks Foundation Model Serving, Microsoft Foundry, GCP Vertex AI, Alibaba Cloud PAI-EAS, and AWS Bedrock. text-davinci is available on the tracked providers still being sourced. Provider coverage can affect latency, region availability, compliance posture, and fallback options.
When should I pick Llama 2 70B Chat over text-davinci?
Llama 2 70B Chat is safer overall; choose text-davinci when provider fit matters. If your workload also depends on provider fit, start with Llama 2 70B Chat; if it depends on provider fit, run the same evaluation with text-davinci.
Continue comparing
Last reviewed: 2026-05-16. Data sourced from public model cards and provider documentation.