GPT-5.3-Codex vs GPT-5.5
GPT-5.3-Codex (2026) and GPT-5.5 (2026) compare a coding-specialized model against a standalone API model. GPT-5.3-Codex ships a 400k-token context window, while GPT-5.5 ships a 1.05m-token context window. On SWE-bench Verified, GPT-5.3-Codex leads by 2.4 pts. On pricing, GPT-5.3-Codex costs $1.75/1M input tokens; GPT-5.5 ranges from $5 to $10/1M input tokens by tier. 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 is coding-specialized model, while GPT-5.5 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 | GPT-5.5 |
|---|---|---|
| Product type | Coding-specialized model | Standalone API model |
| Best for | custom coding agents, code generation, and tool loops | reasoning-heavy apps, multimodal apps, and tool-calling agents |
| Decision fit | Coding, RAG, and Agents | Coding, RAG, and Agents |
| Context window | 400k | 1.05m |
| Cheapest output | $14/1M tokens | $30/1M tokens |
| Provider routes | 3 tracked | 3 tracked |
| Shared benchmarks | SWE-bench Verified leader | 3 rows |
Decision tradeoffs
- GPT-5.3-Codex holds a shared-benchmark lead on SWE-bench Verified, ahead by 2.4 points.
- GPT-5.3-Codex has the lower cheapest tracked output price at $14/1M tokens.
- GPT-5.3-Codex uniquely exposes Computer use in local model data.
- Local decision data tags GPT-5.3-Codex for Coding, RAG, and Agents.
- GPT-5.5 holds a shared-benchmark lead on SWE-bench Pro, ahead by 1.8 points.
- GPT-5.5 has the larger context window for long prompts, retrieval packs, or transcript analysis.
- GPT-5.5 uniquely exposes Multimodal in local model data.
- Local decision data tags GPT-5.5 for Coding, RAG, and Agents.
Monthly cost at traffic
Estimate token spend from the cheapest tracked input and output route or tier on this page.
GPT-5.3-Codex
$4,900
Cheapest tracked route/tier: OpenRouter
GPT-5.5
$11,500
Cheapest tracked route/tier: OpenAI API 0-272K input tokens
Estimated monthly gap: $6,600. Batch, cache, alternate speed tiers, and negotiated pricing are excluded from this local estimate.
Switch friction
- Provider overlap exists on OpenAI API, OpenRouter, and Vercel AI Gateway; start route-level A/B tests there.
- GPT-5.5 is $16/1M tokens higher on cheapest tracked output pricing, so quality gains need to justify the spend.
- Check replacement coverage for Computer use before moving production traffic.
- GPT-5.5 adds Multimodal in local capability data.
- Provider overlap exists on OpenRouter, OpenAI API, and Vercel AI Gateway; start route-level A/B tests there.
- GPT-5.3-Codex is $16/1M tokens lower on cheapest tracked output pricing before cache, batch, or negotiated discounts.
- Check replacement coverage for Multimodal before moving production traffic.
- GPT-5.3-Codex adds Computer use in local capability data.
Specs
| Specification | ||
|---|---|---|
| Released | 2026-02-05 | 2026-04-23 |
| Context window | 400k | 1.05m |
| Parameters | — | — |
| Architecture | decoder only | decoder only |
| License | Proprietary | Proprietary |
| Openness | Proprietary | Proprietary |
| Commercial use | Commercial use with conditions | Commercial use with conditions |
| Knowledge cutoff | 2025-08 | 2025-12 |
Pricing and availability
| Pricing attribute | GPT-5.3-Codex | GPT-5.5 |
|---|---|---|
| Input price | $1.75/1M tokens |
|
| Output price | $14/1M tokens |
|
| Providers |
Capabilities
| Capability | GPT-5.3-Codex | GPT-5.5 |
|---|---|---|
| Vision | Yes | Yes |
| Multimodal | No | Yes |
| Reasoning | Yes | Yes |
| Function calling | Yes | Yes |
| Tool use | Yes | Yes |
| Structured outputs | Yes | Yes |
| Code execution | Yes | Yes |
| IDE integration | No | No |
| Computer use | Yes | No |
| Parallel agents | No | No |
Benchmarks
| Benchmark | GPT-5.3-Codex | GPT-5.5 |
|---|---|---|
| SWE-bench Verified | 85.0 | 82.6 |
| SWE-bench Pro | 56.8 | 58.6 |
| Terminal-Bench 2.0 | 77.3 | 82.7 |
Deep dive
On shared benchmark coverage, SWE-bench Verified has GPT-5.3-Codex at 85 and GPT-5.5 at 82.6, with GPT-5.3-Codex ahead by 2.4 points; SWE-bench Pro has GPT-5.3-Codex at 56.8 and GPT-5.5 at 58.6, with GPT-5.5 ahead by 1.8 points; Terminal-Bench 2.0 has GPT-5.3-Codex at 77.3 and GPT-5.5 at 82.7, with GPT-5.5 ahead by 5.4 points. The largest visible gap is 5.4 points on Terminal-Bench 2.0, which matters most when that benchmark mirrors your workload. Treat isolated benchmark wins as directional, because provider routing, prompt style, and tool access can move real application results.
The capability footprint differs most on multimodal input: GPT-5.5. Both models share vision, reasoning mode, function calling, and tool use, 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 on the cheapest tracked provider, while GPT-5.5 lists tiered pricing: 0-272K input tokens is $5/1M input and $30/1M output; 0-272,000t is $5/1M input and $30/1M output; 272K+ input tokens is $8/1M input and $36/1M output; 272,000t+ is $10/1M input and $45/1M output. A 70/30 input-output blend puts GPT-5.3-Codex lower by about $7.08 per million blended tokens. For tiered rows, this cheapest-track view can understate interactive or fast-lane spend, so compare the tier you will actually use. Availability is 3 providers versus 3, so concentration risk also matters.
Choose GPT-5.3-Codex when coding workflow support and lower input-token cost are central to the workload. Choose GPT-5.5 when coding workflow support and larger context windows are more important. For production, rerun your own prompts through the exact provider, region, and tool stack you plan to ship.
FAQ
Which has a larger context window, GPT-5.3-Codex or GPT-5.5?
GPT-5.5 supports 1.05m tokens, while GPT-5.3-Codex supports 400k tokens. That gap matters most for long documents, large codebases, retrieval-heavy agents, and conversations where earlier context must remain visible. Use this as a quick comparison signal, then confirm the provider-specific limits before committing to production.
Which is cheaper, GPT-5.3-Codex or GPT-5.5?
GPT-5.3-Codex lists $1.75/1M input and $14/1M output tokens on the cheapest tracked provider. GPT-5.5 lists tiered pricing: 0-272K input tokens is $5/1M input and $30/1M output; 0-272,000t is $5/1M input and $30/1M output; 272K+ input tokens is $8/1M input and $36/1M output; 272,000t+ is $10/1M input and $45/1M output. Compare the tier you will actually use; cheap async pricing can overstate savings for interactive workflows. Provider discounts or batch pricing can still change the final bill.
Is GPT-5.3-Codex or GPT-5.5 open source?
GPT-5.3-Codex is listed under Proprietary. GPT-5.5 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 vision, GPT-5.3-Codex or GPT-5.5?
Both GPT-5.3-Codex and GPT-5.5 expose vision. The better choice depends on benchmark fit, context budget, pricing, and whether your provider route exposes the same capability surface. Use this as a quick comparison signal, then confirm the provider-specific limits before committing to production.
Which is better for multimodal input, GPT-5.3-Codex or GPT-5.5?
GPT-5.5 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.
Where can I run GPT-5.3-Codex and GPT-5.5?
GPT-5.3-Codex is available on OpenRouter, OpenAI API, and Vercel AI Gateway. GPT-5.5 is available on OpenAI API, OpenRouter, and Vercel AI Gateway. Provider coverage can affect latency, region availability, compliance posture, and fallback options.
Continue comparing
Last reviewed: 2026-06-10. Data sourced from public model cards and provider documentation.