GPT-5.2 Codex vs Llama 3.1 70B Instruct
GPT-5.2 Codex (2025) and Llama 3.1 70B Instruct (2024) compare a coding-specialized model against a standalone API model. GPT-5.2 Codex ships a not-yet-sourced context window, while Llama 3.1 70B Instruct ships a 128k-token context window. On pricing, Llama 3.1 70B Instruct costs $0.40/1M input tokens versus $1.75/1M for the alternative. This page treats the result as workflow and deployment fit, not a universal model winner.
Treat this as a product-type comparison: GPT-5.2 Codex is coding-specialized model, while Llama 3.1 70B Instruct is standalone API model. Choose based on workflow fit before reading any benchmark or price row as decisive.
Decision scorecard
Local evidence first| Signal | GPT-5.2 Codex | Llama 3.1 70B Instruct |
|---|---|---|
| Product type | Coding-specialized model | Standalone API model |
| Best for | custom coding agents, code generation, and tool loops | provider-routed production |
| Decision fit | Coding, Agents, and Vision | Coding, RAG, and Long context |
| Context window | — | 128k |
| Cheapest output | $14/1M tokens | $0.40/1M tokens |
| Provider routes | 1 tracked | 13 tracked |
| Shared benchmarks | 0 rows | 0 rows |
Decision tradeoffs
- GPT-5.2 Codex uniquely exposes Vision, Multimodal, and Reasoning in local model data.
- Local decision data tags GPT-5.2 Codex for Coding, Agents, and Vision.
- 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 $0.40/1M tokens.
- Llama 3.1 70B Instruct has broader tracked provider coverage for fallback and procurement flexibility.
- Llama 3.1 70B Instruct uniquely exposes Structured outputs in local model data.
- 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 route or tier on this page.
GPT-5.2 Codex
$4,900
Cheapest tracked route/tier: Vercel AI Gateway
Llama 3.1 70B Instruct
$420
Cheapest tracked route/tier: Hyperbolic AI Inference
Estimated monthly gap: $4,480. Batch, cache, alternate speed tiers, and negotiated pricing are excluded from this local estimate.
Switch friction
- Provider overlap exists on Vercel AI Gateway; start route-level A/B tests there.
- Llama 3.1 70B Instruct is $13.60/1M tokens lower on cheapest tracked output pricing before cache, batch, or negotiated discounts.
- Check replacement coverage for Vision, Multimodal, and Reasoning before moving production traffic.
- Llama 3.1 70B Instruct adds Structured outputs in local capability data.
- Provider overlap exists on Vercel AI Gateway; start route-level A/B tests there.
- GPT-5.2 Codex is $13.60/1M tokens higher on cheapest tracked output pricing, so quality gains need to justify the spend.
- Check replacement coverage for Structured outputs before moving production traffic.
- GPT-5.2 Codex adds Vision, Multimodal, and Reasoning in local capability data.
Specs
| Specification | ||
|---|---|---|
| Released | 2025-12-18 | 2024-07-23 |
| Context window | — | 128k |
| Parameters | — | 70B |
| Architecture | decoder only | decoder only |
| License | Proprietary | Llama 3 Community |
| Openness | Proprietary | Open weights |
| Commercial use | Commercial use with conditions | Commercial use with conditions |
| Knowledge cutoff | 2025-08 | 2023-12 |
Pricing and availability
| Pricing attribute | GPT-5.2 Codex | Llama 3.1 70B Instruct |
|---|---|---|
| Input price | $1.75/1M tokens | $0.40/1M tokens |
| Output price | $14/1M tokens | $0.40/1M tokens |
| Providers |
Capabilities
| Capability | GPT-5.2 Codex | Llama 3.1 70B Instruct |
|---|---|---|
| Vision | Yes | No |
| Multimodal | Yes | No |
| Reasoning | Yes | No |
| Function calling | Yes | No |
| Tool use | Yes | 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 vision: GPT-5.2 Codex, multimodal input: GPT-5.2 Codex, reasoning mode: GPT-5.2 Codex, function calling: GPT-5.2 Codex, tool use: GPT-5.2 Codex, structured outputs: Llama 3.1 70B Instruct, and code execution: GPT-5.2 Codex. 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.
For cost, GPT-5.2 Codex lists $1.75/1M input and $14/1M output tokens on the cheapest tracked provider, while Llama 3.1 70B Instruct lists $0.40/1M input and $0.40/1M output tokens on the cheapest tracked provider. A 70/30 input-output blend puts Llama 3.1 70B Instruct lower by about $5.02 per million blended tokens. Availability is 1 providers versus 13, so concentration risk also matters.
Choose GPT-5.2 Codex when coding workflow support are central to the workload. Choose Llama 3.1 70B Instruct when provider fit, lower input-token cost, 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.
FAQ
Which is cheaper, GPT-5.2 Codex or Llama 3.1 70B Instruct?
Llama 3.1 70B Instruct is cheaper on tracked token pricing. GPT-5.2 Codex costs $1.75/1M input and $14/1M output tokens. Llama 3.1 70B Instruct costs $0.40/1M input and $0.40/1M output tokens. Provider discounts or batch pricing can still change the final bill.
Is GPT-5.2 Codex or Llama 3.1 70B Instruct open source?
GPT-5.2 Codex is listed under Proprietary. Llama 3.1 70B Instruct is listed under Llama 3 Community. 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 vision, GPT-5.2 Codex or Llama 3.1 70B Instruct?
GPT-5.2 Codex has the clearer documented vision signal in this comparison. If vision is mission-critical, validate it against the provider endpoint because model-level support and API-level exposure can differ. Use this as a quick comparison signal, then confirm the provider-specific limits before committing to production.
Which is better for multimodal input, GPT-5.2 Codex or Llama 3.1 70B Instruct?
GPT-5.2 Codex has the clearer documented multimodal input signal in this comparison. If multimodal input is mission-critical, validate it against the provider endpoint because model-level support and API-level exposure can differ.
Which is better for reasoning mode, GPT-5.2 Codex or Llama 3.1 70B Instruct?
GPT-5.2 Codex 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.
Where can I run GPT-5.2 Codex and Llama 3.1 70B Instruct?
GPT-5.2 Codex is available on Vercel AI Gateway. Llama 3.1 70B Instruct is available on Cloudflare Workers AI, OctoAI API (Deprecated), Together AI, Fireworks AI, and NVIDIA NIM. 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.