GPT-5.3-Codex-Spark vs Llama 3.2 11B Instruct
GPT-5.3-Codex-Spark (2026) and Llama 3.2 11B Instruct (2025) compare a coding-specialized model against a standalone API model. GPT-5.3-Codex-Spark ships a 131k-token context window, while Llama 3.2 11B Instruct ships a 128k-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: GPT-5.3-Codex-Spark is coding-specialized model, while Llama 3.2 11B 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-5.3-Codex-Spark | Llama 3.2 11B Instruct |
|---|---|---|
| Product type | Coding-specialized model | Standalone API model |
| Best for | custom coding agents, code generation, and tool loops | multimodal apps |
| Decision fit | Coding, RAG, and Agents | RAG, Long context, and Vision |
| Context window | 131k | 128k |
| Cheapest output | - | $0.27/1M tokens |
| Provider routes | 1 tracked | 1 tracked |
| Shared benchmarks | 0 rows | 0 rows |
Decision tradeoffs
- GPT-5.3-Codex-Spark has the larger context window for long prompts, retrieval packs, or transcript analysis.
- GPT-5.3-Codex-Spark uniquely exposes Function calling, Tool use, and Code execution in local model data.
- Local decision data tags GPT-5.3-Codex-Spark for Coding, RAG, and Agents.
- Llama 3.2 11B Instruct uniquely exposes Vision and Multimodal in local model data.
- Local decision data tags Llama 3.2 11B Instruct for RAG, Long context, and Vision.
Monthly cost at traffic
Estimate token spend from the cheapest tracked input and output route or tier on this page.
GPT-5.3-Codex-Spark
Unavailable
No complete token price in local provider data
Llama 3.2 11B Instruct
$228
Cheapest tracked route/tier: AWS Bedrock
Cost delta unavailable until both models have sourced input and output token prices.
Switch friction
- No overlapping tracked provider route is sourced for GPT-5.3-Codex-Spark and Llama 3.2 11B Instruct; plan for SDK, billing, or endpoint changes.
- Check replacement coverage for Function calling, Tool use, and Code execution before moving production traffic.
- Llama 3.2 11B Instruct adds Vision and Multimodal in local capability data.
- No overlapping tracked provider route is sourced for Llama 3.2 11B Instruct and GPT-5.3-Codex-Spark; plan for SDK, billing, or endpoint changes.
- Check replacement coverage for Vision and Multimodal before moving production traffic.
- GPT-5.3-Codex-Spark adds Function calling, Tool use, and Code execution in local capability data.
Specs
| Specification | ||
|---|---|---|
| Released | 2026-02-12 | 2025-09-01 |
| Context window | 131k | 128k |
| Parameters | — | 11B |
| Architecture | decoder only | - |
| License | Proprietary | Llama 3 Community |
| Openness | Proprietary | Open weights |
| Commercial use | Commercial use with conditions | Commercial use with conditions |
| Knowledge cutoff | - | 2023-12 |
Pricing and availability
| Pricing attribute | GPT-5.3-Codex-Spark | Llama 3.2 11B Instruct |
|---|---|---|
| Input price | - | $0.20/1M tokens |
| Output price | - | $0.27/1M tokens |
| Providers |
Capabilities
| Capability | GPT-5.3-Codex-Spark | Llama 3.2 11B Instruct |
|---|---|---|
| Vision | No | Yes |
| Multimodal | No | Yes |
| Reasoning | No | No |
| Function calling | Yes | No |
| Tool use | Yes | No |
| Structured outputs | Yes | 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: Llama 3.2 11B Instruct, multimodal input: Llama 3.2 11B Instruct, function calling: GPT-5.3-Codex-Spark, tool use: GPT-5.3-Codex-Spark, and code execution: GPT-5.3-Codex-Spark. 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.
Pricing coverage is uneven: GPT-5.3-Codex-Spark has no token price sourced yet and Llama 3.2 11B Instruct has $0.20/1M input tokens. Provider availability is 1 tracked routes versus 1. Treat unknown pricing as an integration gap, then verify the route you will actually call before estimating production spend.
Choose GPT-5.3-Codex-Spark when coding workflow support and larger context windows are central to the workload. Choose Llama 3.2 11B Instruct when vision-heavy evaluation 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, GPT-5.3-Codex-Spark or Llama 3.2 11B Instruct?
GPT-5.3-Codex-Spark supports 131k tokens, while Llama 3.2 11B Instruct supports 128k tokens. That gap matters most for long documents, large codebases, retrieval-heavy agents, and conversations where earlier context must remain visible.
Is GPT-5.3-Codex-Spark or Llama 3.2 11B Instruct open source?
GPT-5.3-Codex-Spark is listed under Proprietary. Llama 3.2 11B 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-5.3-Codex-Spark or Llama 3.2 11B Instruct?
Llama 3.2 11B Instruct 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.
Which is better for multimodal input, GPT-5.3-Codex-Spark or Llama 3.2 11B Instruct?
Llama 3.2 11B Instruct 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-Spark or Llama 3.2 11B Instruct?
GPT-5.3-Codex-Spark 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.
Where can I run GPT-5.3-Codex-Spark and Llama 3.2 11B Instruct?
GPT-5.3-Codex-Spark is available on OpenAI API. Llama 3.2 11B Instruct is available on AWS Bedrock. Provider coverage can affect latency, region availability, compliance posture, and fallback options. Use this as a quick comparison signal, then confirm the provider-specific limits before committing to production.
Continue comparing
Last reviewed: 2026-06-04. Data sourced from public model cards and provider documentation.