OpenAI GPT-5.1 Chat: 16K max output, text+image input, AA Math 94.0, accessed via OrcaRouter.
openai/gpt-5.1-chat-latest is a large language model developed by OpenAI, optimized for chat-based interactions. It accepts both text and image inputs and can generate outputs up to 16,384 tokens.…
openai/gpt-5.1-chat-latest is a text and image input model capable of generating human-like responses, code, and structured outputs. Its primary strength is mathematical reasoning, as demonstrated by its 94.0 score on the AA Math benchmark. It can solve complex equations, explain step-by-step solutions, and work with mathematical notation. The model also handles general conversation, instruction following, and creative writing. With 16K maximum output tokens, it can produce long-form content such as detailed reports, multi-turn dialogues, and extensive code segments. Image understanding allows analysis of diagrams, graphs, and handwritten notes, enabling use cases like chart interpretation and visual problem solving.
Optimal use cases include mathematical tutoring, automated theorem proving, and precise data analysis where each step must be verified. The model's 94.0 AA Math score makes it suitable for STEM education platforms that need to generate correct solutions and explanations. Multimodal input allows processing of images containing equations, diagrams, or data visualizations, which is valuable in scientific research and engineering contexts. Long output capacity supports generating exhaustive documentation, pseudo-code, or dialogue systems that require extended context. For general-purpose chat that does not involve heavy reasoning, a cheaper model may be more cost-effective; gpt-5.1-chat-latest is best reserved for tasks that leverage its math and multimodal strengths.
For simple queries, factual lookups, or tasks that do not require mathematical reasoning, the higher cost of this model may not be justified. Output tokens cost $10.00 per 1M tokens – significantly more than input tokens. If your application produces long replies that are not mathematically complex, consider a less expensive model like OpenAI’s GPT-4o mini or GPT-3.5 Turbo, which can handle basic conversation at lower cost. Similarly, if your workload is input-heavy but output-light, the input price ($1.25/1M) is moderate, but still more than alternatives. Use this model strategically for high-value reasoning tasks; for bulk processing of simple queries, route to cheaper models via OrcaRouter's API to optimize spend.
The model accepts images as part of the input, alongside text. Images can be provided as URLs or base64-encoded data in the user message content. The model processes the visual information to extract meaning, such as reading text from a screenshot, interpreting a graph, or understanding a diagram. There is no separate vision model; the multimodal capability is integrated. This is useful for applications like analyzing charts in financial reports, solving geometry problems from handwritten figures, or converting whiteboard photos to text explanations. Note that image processing costs are included in the input token pricing; images are typically tokenized based on resolution. For best results, provide clear, high-resolution images.
The model achieved a score of 94.0 on the AA Math benchmark. AA Math (Advanced Algebra) is a test set of mathematical problems evaluating algebraic reasoning, symbolic manipulation, and arithmetic correctness. A score of 94.0 indicates strong performance on these tasks, though the exact methodology (e.g., few-shot, chain-of-thought) is not specified. For comparison, previous GPT-4 models scored in the 80-90 range on similar math benchmarks. Users should interpret this as evidence of improved mathematical capability, but should verify performance on their own domain-specific math problems, as benchmarks may not cover all real-world scenarios.
Only the AA Math score (94.0) is provided in the available information. No other benchmark results, such as MMLU, HumanEval, or GSM8K, are listed. Therefore, it is not possible to fully assess the model's capabilities across general knowledge, coding, or language understanding. Users are encouraged to run their own evaluation on relevant tasks. Given the high AA Math score, one might expect strong reasoning in algebra-related domains, but caution is warranted when extrapolating to other areas. Check OpenAI's official documentation for a complete set of benchmark results if needed.
Specific latency figures for openai/gpt-5.1-chat-latest are not provided in the available data. Performance can vary based on input size, output length, concurrent request volume, and infrastructure load. When accessed through OrcaRouter, the API uses standard OpenAI-compatible streaming and non-streaming modes. Typical inference times for large models with 16K output capacity can range from seconds to minutes depending on the complexity of the request. Users should conduct their own latency testing with realistic workloads. For applications requiring low latency, consider smaller or distilled models that respond faster. OrcaRouter's API supports streaming to start receiving tokens sooner.
Strengths: Excellent mathematical reasoning (AA Math 94.0), multimodal input (text+image), generous output limit (16K tokens), and premium quality responses. Limitations: Higher cost per output token ($10.00/1M) may be prohibitive for high-volume output use; specific input context window not disclosed; no provided scores for other benchmarks like coding or language understanding; latency may be higher than smaller models; only text and image inputs supported (no audio/video). Additionally, as a closed model, fine-tuning is not publicly available through OrcaRouter. Users should weigh these factors against their requirements.
Pricing is per 1 million tokens: $1.25 for input tokens and $10.00 for output tokens. These rates are the provider (OpenAI) rates applied by OrcaRouter with zero markup. Billing is based on the number of tokens consumed, counting both prompt and generated tokens. Tokens from images are billed at the input token rate based on image resolution (exact tokenization per image is defined by OpenAI). There are no additional fees for API calls beyond token usage. This transparent pricing allows users to estimate costs: for example, a request with 1,000 input tokens and 500 output tokens would cost $0.00125 + $0.005 = $0.00625.
The main trade-off is between output quality and cost. At $10.00 per million output tokens, generating long responses can become expensive quickly. For instance, a 1,000-output-token response costs $0.01. If your application typically generates hundreds of such responses per hour, cost can accumulate. Conversely, input costs are comparatively low ($1.25/1M) so large prompts are less of a concern. For cost-sensitive projects, consider limiting output length via the max_tokens parameter, or using a cheaper model for simpler tasks. OrcaRouter allows routing different requests to different models, so you can reserve this premium model only for high-value reasoning steps.
The provided facts do not mention any discounts, volume pricing, or caching mechanisms. OrcaRouter applies the provider rate with zero markup, meaning no additional charges. However, no information is available about whether OrcaRouter offers token caching (e.g., caching common prefixes). Typically, OpenAI's own API does not cache tokens for billing; each request is billed separately. Users should assume no built-in caching and plan their usage accordingly to avoid repeated large input costs. If caching is critical, consider implementing your own caching layer between your application and OrcaRouter.
To use openai/gpt-5.1-chat-latest, set your API requests to OrcaRouter's base URL: https://api.orcarouter.ai/v1. Include the model parameter as "openai/gpt-5.1-chat-latest". Authentication is via an API key (provided by OrcaRouter) in the standard Authorization header. The API is fully OpenAI-compatible, so any client library that works with OpenAI's chat completions endpoint will work with OrcaRouter. Example with cURL: curl -X POST https://api.orcarouter.ai/v1/chat/completions \ -H "Authorization: Bearer YOUR_API_KEY" \ -H "Content-Type: application/json" \ -d '{"model": "openai/gpt-5.1-chat-latest", "messages": [{"role": "user", "content": "Solve 2x+3=7"}], "max_tokens": 500}'
All standard chat completion parameters from the OpenAI API are supported, including: model (required), messages (array of role/content objects), max_tokens (up to 16384), temperature, top_p, n, stop, presence_penalty, frequency_penalty, logit_bias, user, and stream (boolean). For multimodal requests, the user message content can be an array of text and image parts. image_url can be a URL or base64 data. Example: {"role": "user", "content": [{"type": "text", "text": "Describe this graph"}, {"type": "image_url", "image_url": {"url": "https://example.com/graph.png"}}]}. Ensure the base64 data is prefixed with "data:image/png;base64,".
GPT-4o is a multimodal model with similar capabilities (text+image input) and lower pricing. GPT-4o’s exact pricing is not provided here, but it is generally cheaper than $10.00/1M output. GPT-4o may have broader benchmark coverage. However, openai/gpt-5.1-chat-latest scored 94.0 on AA Math, which might be higher than GPT-4o's math scores (though official comparisons are not given). If your priority is mathematical accuracy, this model may be preferable. If you need general multimodal reasoning at lower cost, GPT-4o could be more economical. OrcaRouter supports both models, so users can test on their own data.
Since this is "gpt-5.1-chat-latest", it is a newer iteration within the GPT-5 series. Specific differences from e.g., gpt-5.0 are not documented. The AA Math score of 94.0 may be an improvement over earlier versions, but no comparative benchmarks are provided. The model retains the same API schema and pricing structure as other GPT-5 models. Users upgrading from an earlier GPT-5 should evaluate performance on their tasks to measure any gain. There is no known change in maximum output (16K) or input modalities. For backward compatibility, the model ID is different, so code changes to the model string are required.
OpenAI-compatible — keep the SDK you already use
https://api.orcarouter.ai/v1from openai import OpenAI
client = OpenAI(
base_url="https://api.orcarouter.ai/v1",
api_key="$ORCAROUTER_API_KEY",
)
response = client.chat.completions.create(
model="openai/gpt-5.1-chat-latest",
messages=[{"role": "user", "content": "Hello"}],
)
print(response.choices[0].message.content)frequency_penaltylogit_biaslogprobsmax_tokensparallel_tool_callspresence_penaltypredictionresponse_formatseedservice_tierstopstreamstructured_outputstemperaturetool_choicetoolstop_logprobstop_p| Input / 1M tokens | $1.25 |
| Output / 1M tokens | $10.00 |
| Cache read / 1M | $0.125 |
| Currency | USD |
Estimate based on list price
Estimate only — actual token counts depend on the provider's tokenizer.
GET /api/public/models/openai/gpt-5.1-chat-latestOpen @misc{orcarouter_gpt_5_1_chat_latest,
title = {openai/gpt-5.1-chat-latest API},
author = {openai},
year = {n.d.},
howpublished = {OrcaRouter},
url = {https://www.orcarouter.ai/models/openai/gpt-5.1-chat-latest}
}openai. (n.d.). openai/gpt-5.1-chat-latest API. OrcaRouter. https://www.orcarouter.ai/models/openai/gpt-5.1-chat-latest