openai
OpenAI: GPT-5.4 (batch)
openai/gpt-5.4:batch
Our take
AI-assisted analysis based on the sources below. Suitability is an editorial assessment; test it on your own workload.
OpenRouter's catalog lists openai/gpt-5.4:batch, pointing to the canonical release openai/gpt-5.4-20260305, and describes it as OpenAI's frontier model merging the Codex and GPT lines. The listing reports text, image, and file inputs with text output, a context window above one million tokens, and optional reasoning with several effort levels. All evidence here is catalog-reported; no independent model card was included, so validate capabilities on your own workloads.[1]
Strengths
- The catalog reports multimodal input covering text, images, and files with text output, which suits mixed-content document pipelines.[1]
- According to the listing, reasoning is optional and tunable across effort levels from none up to an extra-high tier, alongside tool calling, structured outputs, seed, and verbosity parameters.[1]
- OpenRouter reports a context window exceeding one million tokens, which could support very long documents or large batched inputs.[1]
Limitations
- Evidence is limited to a single OpenRouter catalog entry; no independent model card was available in this source pack, so capabilities remain vendor- and aggregator-reported.[1]
- The arena results the catalog reports are mixed and category-dependent rather than uniformly strong, so measured quality may vary by task type.[1]
- The listing specifies no batch turnaround guarantees, knowledge cutoff, or deprecation date, leaving operational behavior for scheduled jobs unverified.[1]
Where to use it
- Potentially a fit for offline bulk work such as summarization, extraction, or classification of large text, image, or file collections; confirm throughput and accuracy on representative data first.[1]
- May suit long-document analysis, code review, or repository-scale tasks that benefit from the reported large context window, subject to workload validation.[1]
- Could serve builders of agents or pipelines needing structured outputs and tool use, though teams should test latency and reasoning-effort settings before production.[1]
Sources
- [1] OpenRouter catalog · OpenAI: GPT-5.4 (batch)Sources checked: Nov 13, 2026
About this model
Description below is the original text supplied by OpenRouter, shown in the source language.
GPT-5.4 is OpenAI’s latest frontier model, unifying the Codex and GPT lines into a single system. It features a 1M+ token context window (922K input, 128K output) with support for...
Specifications
- Model ID
- openai/gpt-5.4:batch
- Canonical ID
- openai/gpt-5.4-20260305
- Creator
- openai
- Added to OpenRouter
- 05.03.2026
- Context length
- 1,050,000
- Modality
- text+image+file->text
- Input modalities
- text, image, file
- Output modalities
- text
- Token pricing
- USD per 1M tokens: in $1.25 / out $7.5
- Knowledge cutoff
- —
- Expiration date
- —
Tasks and capabilities
A grade appears only where the source supplies a matching measurement for that task, the model is active and the cohort holds at least 10 comparable models. The capability column lists facts the source declares — supported formats, reasoning and tool parameters — which are not measured quality.
| Task | Grade | Source-declared capability |
|---|---|---|
| General quality | No measurement | reasoning: optional · default medium · efforts: xhigh, high, medium, low, none · parameters: include_reasoning, reasoning, reasoning_effort |
| Coding | AA — next 20% of the cohort | — |
| Agents | No measurement | parameters: structured_outputs, tool_choice, tools |
| Writing | No measurement | output: text |
| Reasoning | No measurement | reasoning: optional · default medium · efforts: xhigh, high, medium, low, none · parameters: include_reasoning, reasoning, reasoning_effort |
| Document analysis | No measurement | input: file |
| Vision | No measurement | input: image |
| Image generation | No measurement | — |
| Audio | No measurement | — |
| Video | No measurement | — |
| Embeddings | No measurement | — |
Evaluations
| Source | Benchmark | Value | Grade | Data retrieved |
|---|---|---|---|---|
| Artificial Analysis | Coding index | 71.1 | AA — next 20% of the cohort | 13.11.2026 |
| Design Arena | models · 3dwin rate 42.3% · rank 96 | 1,084Elo | CC — below the B band | 13.11.2026 |
| Design Arena | models · asciiartwin rate 55.4% · rank 28 | 1,194Elo | BB — next 30% of the cohort | 13.11.2026 |
| Design Arena | models · codecategorieswin rate 52.5% · rank 63 | 1,203Elo | BB — next 30% of the cohort | 13.11.2026 |
| Design Arena | models · datavizwin rate 56.6% · rank 43 | 1,233Elo | BB — next 30% of the cohort | 13.11.2026 |
| Design Arena | models · gamedevwin rate 57.6% · rank 44 | 1,229Elo | BB — next 30% of the cohort | 13.11.2026 |
| Design Arena | models · svgwin rate 57.8% · rank 35 | 1,185Elo | BB — next 30% of the cohort | 13.11.2026 |
| Design Arena | models · uicomponentwin rate 57.4% · rank 48 | 1,232Elo | BB — next 30% of the cohort | 13.11.2026 |
| Design Arena | models · websitewin rate 52.5% · rank 64 | 1,213Elo | BB — next 30% of the cohort | 13.11.2026 |
| Design Arena | agents · androidnativewin rate 47.7% · rank 42 | 1,021Elo | CC — below the B band | 13.11.2026 |
| Design Arena | agents · fullstackwin rate 40.9% · rank 41 | 995Elo | CC — below the B band | 13.11.2026 |
| Design Arena | agents · godotgamedevwin rate 46.9% · rank 24 | 1,135Elo | CC — below the B band | 13.11.2026 |
| Design Arena | agents · mobileappswin rate 46.1% · rank 47 | 1,081Elo | CC — below the B band | 13.11.2026 |
| Design Arena | agents · webappswin rate 40.3% · rank 43 | 1,041Elo | CC — below the B band | 13.11.2026 |
How grades work
Each letter grade compares a model only against other models scored by the same source and metric in the current snapshot. S means the top 10%, A the next 20%, B the next 30%, C the rest. Models without a published measurement are shown as not rated — a supported format alone never implies measured quality.
- S — S — top 10% of the cohort
- A — A — next 20% of the cohort
- B — B — next 30% of the cohort
- C — C — below the B band
Providers
Optional live provider statistics from OpenRouter. Missing values are unknown, not zero.
| Provider | Context length | Price in /1M | Price out /1M | Latency p50 (30m, s) | Throughput p50 (30m, tok/s) | Uptime (30m) |
|---|---|---|---|---|---|---|
| OpenAI | 1,050,000 | $1.25 | $7.5 | — | — | — |
Provider data retrieved: 13.11.2026
Related articles
No published articles identify this model yet.