Codex 1 vs Llama 2 70B Chat
Codex 1 (2025) and Llama 2 70B Chat (2023) compare a coding-specialized model against a standalone API model. Codex 1 ships a 192K-token context window, while Llama 2 70B Chat ships a 4K-token context window. This page treats the result as workflow and deployment fit, not a universal model winner.
Treat this as a product-type comparison: Codex 1 is coding-specialized model, while Llama 2 70B Chat is standalone API model. Choose based on workflow fit before reading any benchmark or price row as decisive.
Decision scorecard
Local evidence first| Signal | Codex 1 | Llama 2 70B Chat |
|---|---|---|
| Product type | Coding-specialized model | Standalone API model |
| Best for | custom coding agents and code generation | provider-routed production |
| Decision fit | Coding, Agents, and Long context | Classification and JSON / Tool use |
| Context window | 192K | 4K |
| Cheapest output | - | $1.50/1M tokens |
| Provider routes | 0 tracked | 14 tracked |
| Shared benchmarks | 0 rows | 0 rows |
Decision tradeoffs
- Codex 1 has the larger context window for long prompts, retrieval packs, or transcript analysis.
- Codex 1 uniquely exposes Reasoning and Code execution in local model data.
- Local decision data tags Codex 1 for Coding, Agents, and Long context.
- 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.
Monthly cost at traffic
Estimate token spend from the cheapest tracked input and output route or tier on this page.
Codex 1
Unavailable
No complete token price in local provider data
Llama 2 70B Chat
$775
Cheapest tracked route/tier: Databricks Foundation Model Serving
Cost delta unavailable until both models have sourced input and output token prices.
Switch friction
- No overlapping tracked provider route is sourced for Codex 1 and Llama 2 70B Chat; plan for SDK, billing, or endpoint changes.
- Check replacement coverage for Reasoning and Code execution before moving production traffic.
- Llama 2 70B Chat adds Structured outputs in local capability data.
- No overlapping tracked provider route is sourced for Llama 2 70B Chat and Codex 1; plan for SDK, billing, or endpoint changes.
- Check replacement coverage for Structured outputs before moving production traffic.
- Codex 1 adds Reasoning and Code execution in local capability data.
Specs
| Specification | ||
|---|---|---|
| Released | 2025-05-16 | 2023-07-18 |
| Context window | 192K | 4K |
| Parameters | — | 70B |
| Architecture | decoder only | decoder only |
| License | Proprietary | Open Source |
| Knowledge cutoff | - | - |
Pricing and availability
| Pricing attribute | Codex 1 | Llama 2 70B Chat |
|---|---|---|
| Input price | - | $0.50/1M tokens |
| Output price | - | $1.50/1M tokens |
| Providers | - |
Capabilities
| Capability | Codex 1 | Llama 2 70B Chat |
|---|---|---|
| Vision | No | No |
| Multimodal | No | No |
| Reasoning | Yes | No |
| Function calling | No | No |
| Tool use | No | No |
| Structured outputs | No | Yes |
| Code execution | Yes | 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 reasoning mode: Codex 1, structured outputs: Llama 2 70B Chat, and code execution: Codex 1. 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: Codex 1 has no token price sourced yet and Llama 2 70B Chat has $0.50/1M input tokens. Provider availability is 0 tracked routes versus 14. Treat unknown pricing as an integration gap, then verify the route you will actually call before estimating production spend.
Choose Codex 1 when coding workflow support and larger context windows are central to the workload. Choose Llama 2 70B Chat 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 has a larger context window, Codex 1 or Llama 2 70B Chat?
Codex 1 supports 192K tokens, while Llama 2 70B Chat supports 4K tokens. That gap matters most for long documents, large codebases, retrieval-heavy agents, and conversations where earlier context must remain visible.
Is Codex 1 or Llama 2 70B Chat open source?
Codex 1 is listed under Proprietary. Llama 2 70B Chat 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 reasoning mode, Codex 1 or Llama 2 70B Chat?
Codex 1 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 structured outputs, Codex 1 or Llama 2 70B Chat?
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.
Which is better for code execution, Codex 1 or Llama 2 70B Chat?
Codex 1 has the clearer documented code execution signal in this comparison. If code execution is mission-critical, validate it against the provider endpoint because model-level support and API-level exposure can differ.
Where can I run Codex 1 and Llama 2 70B Chat?
Codex 1 is available on the tracked providers still being sourced. Llama 2 70B Chat is available on Databricks Foundation Model Serving, Microsoft Foundry, GCP Vertex AI, Alibaba Cloud PAI-EAS, and AWS Bedrock. Provider coverage can affect latency, region availability, compliance posture, and fallback options.
Continue comparing
Last reviewed: 2026-05-16. Data sourced from public model cards and provider documentation.