Technology comparison

GPT-4o vs Claude 3.5 Sonnet: models, context & API pricing

Choose GPT-4o for broad multimodal work, mature OpenAI tooling, and tight integration with the OpenAI ecosystem, including real-time voice and vision tasks. Choose Claude 3.5 Sonnet for long-document reasoning, careful writing, and coding workflows where Anthropic's safety tuning and large context fit well. Both are strong chatbots; verify current context limits, benchmark scores, and per-token prices against official docs before committing, because these figures are version- and date-sensitive.

Technical context and trade-offs

These differences are drawn directly from the comparison criteria below.

Context window: practical length limits for prompts, files, and conversation history.

GPT-4o: GPT-4o is generally documented with a 128K-token context window, which supports large prompts and multi-turn conversations, though effective usable length depends on the model version and API configuration. Exact limits and per-model variants should be confirmed against OpenAI's current documentation, since OpenAI has adjusted model endpoints over time.

Claude 3.5 Sonnet: Claude 3.5 Sonnet was published with a substantially larger context window (commonly cited as 200K tokens), making it attractive for long documents and codebase analysis. Anthropic has offered extended-context options on some plans and API configurations, so the exact limit depends on the version and access tier and should be verified with Anthropic's official docs.

Benchmark performance: published MMLU, HumanEval, and similar results where comparable.

GPT-4o: GPT-4o has published results on standard benchmarks such as MMLU and HumanEval, but exact scores vary by model snapshot, evaluation setup, and prompting method. Treat any single number as indicative rather than definitive and check OpenAI's current model cards for the specific version you plan to use.

Claude 3.5 Sonnet: Claude 3.5 Sonnet has also published MMLU and HumanEval figures and has been widely noted for strong coding performance in vendor-reported and third-party evaluations. Because scores depend on version, harness, and evaluation protocol, comparisons should use matched settings and Anthropic's current documentation rather than isolated numbers.

Quick verdictThe bottom line

GPT-4oClaude 3.5 SonnetExplore the detailed comparison

Scores by criterion

Performance75–25 (2 criteria)

Ease of useNo direct evidence in the criteria

EcosystemNo direct evidence in the criteria

PopularityNo direct evidence in the criteria

GPT-4oClaude 3.5 Sonnet

Scores reflect wins in matching criteria; ties count as half a point.

Technical feature matrix

Criterion	GPT-4o	Claude 3.5 Sonnet	Edge
Context window: practical length limits for prompts, files, and conversation history.	GPT-4o is generally documented with a 128K-token context window, which supports large prompts and multi-turn conversations, though effective usable length depends on the model version and API configuration. Exact limits and per-model variants should be confirmed against OpenAI's current documentation, since OpenAI has adjusted model endpoints over time.	Claude 3.5 Sonnet was published with a substantially larger context window (commonly cited as 200K tokens), making it attractive for long documents and codebase analysis. Anthropic has offered extended-context options on some plans and API configurations, so the exact limit depends on the version and access tier and should be verified with Anthropic's official docs.	Claude 3.5 Sonnet
Benchmark performance: published MMLU, HumanEval, and similar results where comparable.	GPT-4o has published results on standard benchmarks such as MMLU and HumanEval, but exact scores vary by model snapshot, evaluation setup, and prompting method. Treat any single number as indicative rather than definitive and check OpenAI's current model cards for the specific version you plan to use.	Claude 3.5 Sonnet has also published MMLU and HumanEval figures and has been widely noted for strong coding performance in vendor-reported and third-party evaluations. Because scores depend on version, harness, and evaluation protocol, comparisons should use matched settings and Anthropic's current documentation rather than isolated numbers.	Comparable
API pricing per 1M input and output tokens.	GPT-4o pricing is published by OpenAI and is commonly expressed as separate input and output token rates per million tokens. Rates have changed over time and differ across model snapshots, cached inputs, and batch options, so confirm the current rate on OpenAI's pricing page before budgeting.	Claude 3.5 Sonnet pricing is published by Anthropic and similarly uses separate input and output rates per million tokens, with some tiers differing by model version and usage context. Check Anthropic's pricing documentation for the active rate, since token prices are date- and version-sensitive and may change with new releases.	Comparable
Latency and throughput for interactive and batch applications.	GPT-4o is designed for low-latency conversational use and is often used in real-time voice and multimodal applications. Actual latency depends on region, endpoint, prompt size, output length, and load, so it should be measured in your own environment rather than assumed from vendor descriptions.	Claude 3.5 Sonnet generally offers responsive performance, but its larger context and careful reasoning behavior can increase time to first token on very long inputs. As with any hosted model, measure latency under your own workload and region because provider capacity and configuration affect real results.	GPT-4o
Model access and open versus closed availability.	GPT-4o is a closed, proprietary model available through OpenAI's API and consumer products, with some usage subject to account tiers and rate limits. It is not distributed as open weights, so deployments depend on OpenAI's terms and platform availability.	Claude 3.5 Sonnet is a closed, proprietary model offered through Anthropic's API and products, with access governed by Anthropic's terms and rate limits. It is not an open-weights release, so you cannot self-host it and should plan around the provider's availability and data policies.	Comparable
Multimodal capabilities: text, image, and audio inputs.	GPT-4o is natively multimodal across text, image, and audio in supported endpoints, which makes it suitable for vision-heavy products and real-time voice experiences. Feature availability depends on the specific API endpoint, region, and account tier, so verify support for the modalities you need.	Claude 3.5 Sonnet accepts text and image inputs and is strong at document and image understanding, though audio support is not part of its core published capabilities. Confirm current multimodal support in Anthropic's documentation, because available input types can change with model versions and platform updates.	GPT-4o
Practical use cases: coding, writing, document analysis, and production integration.	GPT-4o fits broad product integration, multimodal assistants, voice interfaces, and general coding or writing tasks with mature SDKs and tooling. Its ecosystem and function-calling support make it convenient for teams already building on OpenAI APIs.	Claude 3.5 Sonnet is often favored for long-context document review, careful editing, and coding tasks where coherent reasoning over large files matters. Teams should validate quality on their own prompts, since practical fit depends on task type, data sensitivity, and workflow requirements.	Comparable

When to choose each option

Choose this option if… GPT-4o

Choose GPT-4o when the strengths described here match your project requirements. Latency and throughput for interactive and batch applications.: GPT-4o is designed for low-latency conversational use and is often used in real-time voice and multimodal applications. Actual latency depends on region, endpoint, prompt size, output length, and load, so it should be measured in your own environment rather than assumed from vendor descriptions. Multimodal capabilities: text, image, and audio inputs.: GPT-4o is natively multimodal across text, image, and audio in supported endpoints, which makes it suitable for vision-heavy products and real-time voice experiences. Feature availability depends on the specific API endpoint, region, and account tier, so verify support for the modalities you need.

Choose this option if… Claude 3.5 Sonnet

Choose Claude 3.5 Sonnet when the strengths described here match your project requirements. Context window: practical length limits for prompts, files, and conversation history.: Claude 3.5 Sonnet was published with a substantially larger context window (commonly cited as 200K tokens), making it attractive for long documents and codebase analysis. Anthropic has offered extended-context options on some plans and API configurations, so the exact limit depends on the version and access tier and should be verified with Anthropic's official docs.

Technology role diagram

Option A

GPT-4o

AI & LLMs

Option B

Claude 3.5 Sonnet

Comparison

This diagram shows broad areas of use, not a complete deployment architecture.

Code examples

GPT-4o

from openai import OpenAI

client = OpenAI()
response = client.chat.completions.create(
    model="gpt-4o",
    messages=[{"role": "user", "content": "Hello, world!"}]
)
print(response.choices[0].message.content)

Frequently asked questions

Which model is better for coding tasks in 2026?

Both GPT-4o and Claude 3.5 Sonnet are capable coding assistants, and neither is universally better. Claude 3.5 Sonnet has been widely used for refactoring and long-file reasoning, while GPT-4o benefits from broad OpenAI tooling and multimodal inputs. Test both on your own repository, prompts, and language versions before deciding.

Does Claude 3.5 Sonnet have a larger context window than GPT-4o?

Claude 3.5 Sonnet is commonly documented with a 200K-token context window, while GPT-4o is generally documented at 128K tokens. Exact limits can vary by model version, access tier, and API configuration, so confirm the current numbers in the providers' official documentation before designing a pipeline around them.

How do the API prices compare per 1M tokens?

Both providers publish separate input and output token rates per million tokens, but the numbers change with model versions, cached inputs, and batch options. Rather than relying on a static figure, check OpenAI's and Anthropic's current pricing pages and calculate cost using your expected input-output ratio.

Is either model open source or self-hostable?

No. GPT-4o is a closed model available through OpenAI, and Claude 3.5 Sonnet is a closed model available through Anthropic. Neither is distributed as open weights, so production use depends on provider terms, rate limits, and data-handling policies rather than self-hosting.

Which model should I choose for analyzing long documents?

Claude 3.5 Sonnet is often a practical first choice for very long documents because of its larger documented context window. However, effective retrieval quality depends on how you chunk, prompt, and validate outputs, and GPT-4o may perform comparably on well-structured tasks. Benchmark both on representative documents before committing.

How do latency and throughput compare?

GPT-4o is designed for low-latency interactive and multimodal applications, while Claude 3.5 Sonnet can take longer on very long inputs because of its larger context and reasoning behavior. Actual latency depends on region, prompt size, output length, and provider load, so measure it in your own environment instead of assuming vendor numbers.

Can both models handle images and audio?

GPT-4o supports text, image, and audio modalities in supported endpoints, making it a strong fit for voice and vision products. Claude 3.5 Sonnet supports text and image inputs, with audio not part of its core published capabilities. Verify modality support and regional availability in each provider's current documentation.

Which model is the safer default for a production integration?

Neither is universally safer; the choice depends on your data policies, compliance requirements, and workload. Both are closed hosted models with enterprise options and changing terms, so review each provider's security documentation, data-retention settings, and rate limits, then run a small pilot before committing.