GPT-5.4 vs GPT-5.5
GPT-5.4 (2026) and GPT-5.5 (2026) are frontier-tier reasoning models from OpenAI. GPT-5.4 ships a 1.1M-token context window, while GPT-5.5 ships a 1.1M-token context window. On MMLU PRO, GPT-5.5 leads by a hair. On pricing, GPT-5.4 costs $2.5/1M input tokens versus $5/1M for the alternative. This comparison covers specs, pricing, capabilities, benchmarks, provider availability, and production fit. It focuses on practical selection signals rather than broad model-family marketing.
GPT-5.4 is ~100% cheaper at $2.5/1M; pay for GPT-5.5 only for coding workflow support.
Decision scorecard
Local evidence first| Signal | GPT-5.4 | GPT-5.5 |
|---|---|---|
| Decision fit | Coding, RAG, and Agents | Coding, RAG, and Agents |
| Context window | 1.1M | 1.1M |
| Cheapest output | $20/1M tokens | $30/1M tokens |
| Provider routes | 2 tracked | 2 tracked |
| Shared benchmarks | 5 rows | MMLU PRO leader |
Decision tradeoffs
- GPT-5.4 has the lower cheapest tracked output price at $20/1M tokens.
- Local decision data tags GPT-5.4 for Coding, RAG, and Agents.
- GPT-5.5 leads the largest shared benchmark signal on MMLU PRO by 0.6 points.
- GPT-5.5 uniquely exposes Vision 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 prices on this page.
GPT-5.4
$7,000
Cheapest tracked route: OpenAI API
GPT-5.5
$11,500
Cheapest tracked route: OpenAI API
Estimated monthly gap: $4,500. Batch, cache, and negotiated pricing are excluded from this local estimate.
Switch friction
- Provider overlap exists on OpenAI API and OpenRouter; start route-level A/B tests there.
- GPT-5.5 is $10/1M tokens higher on cheapest tracked output pricing, so quality gains need to justify the spend.
- GPT-5.5 adds Vision in local capability data.
- Provider overlap exists on OpenAI API and OpenRouter; start route-level A/B tests there.
- GPT-5.4 is $10/1M tokens lower on cheapest tracked output pricing before cache, batch, or negotiated discounts.
- Check replacement coverage for Vision before moving production traffic.
Specs
Pricing and availability
| Pricing attribute | GPT-5.4 | GPT-5.5 |
|---|---|---|
| Input price | $2.5/1M tokens | $5/1M tokens |
| Output price | $20/1M tokens | $30/1M tokens |
| Providers |
Capabilities
| Capability | GPT-5.4 | GPT-5.5 |
|---|---|---|
| Vision | No | Yes |
| Multimodal | Yes | Yes |
| Reasoning | Yes | Yes |
| Function calling | Yes | Yes |
| Tool use | Yes | Yes |
| Structured outputs | Yes | Yes |
| Code execution | Yes | Yes |
Benchmarks
| Benchmark | GPT-5.4 | GPT-5.5 |
|---|---|---|
| MMLU PRO | 87.5 | 88.1 |
| SWE-bench Verified | 71.7 | 88.7 |
| Google-Proof Q&A | 92.0 | 93.6 |
| Chatbot Arena | 1479.0 | 1488.0 |
| SWE-bench Pro | 57.7 | 58.6 |
Deep dive
On shared benchmark coverage, MMLU PRO has GPT-5.4 at 87.5 and GPT-5.5 at 88.1, with GPT-5.5 ahead by 0.6 points; SWE-bench Verified has GPT-5.4 at 71.7 and GPT-5.5 at 88.7, with GPT-5.5 ahead by 17 points; Google-Proof Q&A has GPT-5.4 at 92 and GPT-5.5 at 93.6, with GPT-5.5 ahead by 1.6 points. The largest visible gap is 17 points on SWE-bench Verified, 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 vision: GPT-5.5. Both models share multimodal input, 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-5.4 lists $2.5/1M input and $20/1M output tokens, while GPT-5.5 lists $5/1M input and $30/1M output tokens on the cheapest tracked provider. A 70/30 input-output blend puts GPT-5.4 lower by about $4.75 per million blended tokens. Availability is 2 providers versus 2, so concentration risk also matters.
Choose GPT-5.4 when coding workflow support and lower input-token cost are central to the workload. Choose GPT-5.5 when coding workflow support 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-5.4 or GPT-5.5?
GPT-5.4 supports 1.1M tokens, while GPT-5.5 supports 1.1M 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-5.4 or GPT-5.5?
GPT-5.4 is cheaper on tracked token pricing. GPT-5.4 costs $2.5/1M input and $20/1M output tokens. GPT-5.5 costs $5/1M input and $30/1M output tokens. Provider discounts or batch pricing can still change the final bill.
Is GPT-5.4 or GPT-5.5 open source?
GPT-5.4 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-5.4 or GPT-5.5?
GPT-5.5 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-5.4 or GPT-5.5?
Both GPT-5.4 and GPT-5.5 expose multimodal input. 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.
Where can I run GPT-5.4 and GPT-5.5?
GPT-5.4 is available on OpenAI API and OpenRouter. GPT-5.5 is available on OpenAI API and OpenRouter. 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: 2026-05-11. Data sourced from public model cards and provider documentation.