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GPT-5.3-Codex vs Llama 3.2 1B Instruct

GPT-5.3-Codex (2026) and Llama 3.2 1B Instruct (2024) are agentic coding models from OpenAI and AI at Meta. GPT-5.3-Codex ships a not-yet-sourced context window, while Llama 3.2 1B Instruct ships a 128K-token context window. On pricing, Llama 3.2 1B Instruct costs $0.03/1M input tokens versus $1.75/1M for the alternative. This comparison covers specs, pricing, capabilities, benchmarks, provider availability, and production fit.

Llama 3.2 1B Instruct is ~6381% cheaper at $0.03/1M; pay for GPT-5.3-Codex only for coding workflow support.

Specs

Released2026-02-052024-09-25
Context window128K
Parameters1.23B
Architecturedecoder onlydecoder only
LicenseProprietaryOpen Source
Knowledge cutoff-2023-12

Pricing and availability

GPT-5.3-CodexLlama 3.2 1B Instruct
Input price$1.75/1M tokens$0.03/1M tokens
Output price$14/1M tokens$0.2/1M tokens
Providers

Capabilities

GPT-5.3-CodexLlama 3.2 1B Instruct
Vision
Multimodal
Reasoning
Function calling
Tool use
Structured outputs
Code execution

Benchmarks

No shared benchmark rows are currently sourced for this pair.

Deep dive

The capability footprint differs most on multimodal input: GPT-5.3-Codex, function calling: GPT-5.3-Codex, tool use: GPT-5.3-Codex, and code execution: GPT-5.3-Codex. Both models share 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-5.3-Codex lists $1.75/1M input and $14/1M output tokens, while Llama 3.2 1B Instruct lists $0.03/1M input and $0.2/1M output tokens on the cheapest tracked provider. A 70/30 input-output blend puts Llama 3.2 1B Instruct lower by about $5.35 per million blended tokens. Availability is 1 providers versus 5, so concentration risk also matters.

Choose GPT-5.3-Codex when coding workflow support are central to the workload. Choose Llama 3.2 1B 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. 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, GPT-5.3-Codex or Llama 3.2 1B Instruct?

Llama 3.2 1B Instruct is cheaper on tracked token pricing. GPT-5.3-Codex costs $1.75/1M input and $14/1M output tokens. Llama 3.2 1B Instruct costs $0.03/1M input and $0.2/1M output tokens. Provider discounts or batch pricing can still change the final bill.

Is GPT-5.3-Codex or Llama 3.2 1B Instruct open source?

GPT-5.3-Codex is listed under Proprietary. Llama 3.2 1B 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 multimodal input, GPT-5.3-Codex or Llama 3.2 1B Instruct?

GPT-5.3-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 function calling, GPT-5.3-Codex or Llama 3.2 1B Instruct?

GPT-5.3-Codex has the clearer documented function calling signal in this comparison. If function calling is mission-critical, validate it against the provider endpoint because model-level support and API-level exposure can differ.

Which is better for tool use, GPT-5.3-Codex or Llama 3.2 1B Instruct?

GPT-5.3-Codex has the clearer documented tool use signal in this comparison. If tool use is mission-critical, validate it against the provider endpoint because model-level support and API-level exposure can differ.

Where can I run GPT-5.3-Codex and Llama 3.2 1B Instruct?

GPT-5.3-Codex is available on OpenRouter. Llama 3.2 1B Instruct is available on OpenRouter, Fireworks AI, NVIDIA NIM, Bitdeer AI, and AWS Bedrock. Provider coverage can affect latency, region availability, compliance posture, and fallback options.

Continue comparing

Last reviewed: 2026-04-24. Data sourced from public model cards and provider documentation.