PC Slower Than It Used to Be?
A free scan shows the junk files, broken settings and background clutter dragging Windows down - then fixes them in one click.Free scan · Windows 10 & 11Crashes, No Sound, or Screen Glitches?
Random freezes, missing sound and display glitches usually trace back to one bad driver. Find and replace yours safely.Free scan · under a minuteSome links on this page are affiliate links: if you buy through them we may earn a commission, at no extra cost to you.
There was no universal winner. In the 2024 head-to-head comparison, Claude 3.5 Sonnet generally made the stronger case for coding, long-form writing and several text-reasoning benchmarks. GPT-4o was the more versatile multimodal product, with native text, image, audio and speech capabilities plus deeper integration into ChatGPT.
That distinction matters even more now: as of August 2026, both GPT-4o and Claude 3.5 Sonnet are legacy comparison targets rather than obvious current choices for a new production system. Their original results remain useful for understanding the trade-offs, but availability, pricing and recommended models may have changed.
GPT-4o vs Claude 3.5 Sonnet at a glance
| Category | GPT-4o | Claude 3.5 Sonnet |
|---|---|---|
| Provider | OpenAI | Anthropic |
| Original comparison period | May 2024 onward | June 2024 onward |
| Representative API snapshot | gpt-4o-2024-08-06 |
claude-3-5-sonnet-20240620 |
| Later relevant snapshot | gpt-4o-2024-11-20 |
claude-3-5-sonnet-20241022 |
| Context window at launch | 128,000 tokens | 200,000 tokens |
| Launch-era API input price | $5 per million tokens | $3 per million tokens |
| Launch-era API output price | $15 per million tokens | $15 per million tokens |
| Strongest distinction | Integrated multimodal interaction, including audio and speech | Text quality, coding, long-context work and instruction following |
The prices and context limits above are historical launch-era figures, not a current price recommendation. See OpenAI’s GPT-4o documentation and Anthropic’s Claude 3.5 Sonnet announcement for the original model details.
The Tool Desk
Outbyte PC Repair FREEClear out junk files and repair common Windows errorsFree Scan →Outbyte Driver Updater FREEFix the driver behind crashes, sound loss and screen glitchesFind Drivers →What exactly is being compared?
“Claude 3.5” is not precise enough for a fair comparison. This article refers specifically to Claude 3.5 Sonnet, not Claude 3.5 Haiku. Anthropic released the original Sonnet version in June 2024, identified in API documentation as claude-3-5-sonnet-20240620, and later released an updated version, claude-3-5-sonnet-20241022.
#1 Best Overall
On the OpenAI side, the comparison is with GPT-4o, preferably a dated API snapshot such as gpt-4o-2024-08-06. GPT-4o also had later snapshots, including gpt-4o-2024-11-20.
A model in the ChatGPT or Claude consumer app is not necessarily identical to a dated API model. The app may add browsing, memory, file handling, voice, routing, system instructions and other product features. API testing is more reproducible when a dated snapshot is used, while a consumer-app result describes the whole product experience rather than raw model capability. OpenAI explains the role of model snapshots in its API model documentation.
What the benchmark evidence actually showed
Claude 3.5 Sonnet’s published results were highly competitive with, and on several text benchmarks better than, GPT-4o. Anthropic reported the original Sonnet at approximately:
Quick wins for a faster PC:
Scan for outdated or missing drivers - takes under a minuteDriver Scan →Clear out junk files and repair common Windows errorsFree Scan →- 59.4% on GPQA Diamond under the cited zero-shot chain-of-thought setup.
- 88.3% on MMLU under the cited zero-shot chain-of-thought setup.
- 71.1% on MATH under the cited setup.
- 92.0% on HumanEval for Python coding tasks.
The same Anthropic model-card material lists GPT-4o at 88.7% on MMLU in the referenced OpenAI evaluation. That number should not be read as a direct head-to-head result: the models were not necessarily tested with the same prompts, number of examples, evaluator or procedure. Anthropic’s published methodology illustrates why benchmark tables need their test conditions alongside their scores. See the Claude model card.
An independent Stanford HELM MMLU evaluation later reported:
- Claude 3.5 Sonnet, October 2024: 0.873.
- GPT-4o, August 2024 snapshot: 0.843.
That supports the conclusion that Claude often had an advantage on this particular evaluation. It does not establish a universal ranking across every task, model snapshot or product surface. The Stanford HELM results are more useful than a vendor score in isolation because they provide an independent evaluation context, but even they measure only a defined benchmark.
Why benchmark scores are easy to misread
A benchmark result can change with the:
- Model snapshot and release date.
- System prompt and user prompt.
- Zero-shot, few-shot or chain-of-thought format.
- Sampling temperature and number of attempts.
- Use of majority voting, tools, browsing or code execution.
- Benchmark version and evaluation harness.
- Provider-reported or independently reproduced methodology.
For example, a score from a raw model answering a question is not equivalent to a score from an agent that can search files, execute code, retry failed patches and run tests. A responsible comparison therefore says “Claude performed better on this benchmark under this setup,” not simply “Claude is smarter.”
Free tools Windows power users keep installed
One-click scans. No signup required.
Rank #2
Coding: Claude usually had the edge, but the task matters
Claude 3.5 Sonnet had the stronger reputation and several strong results for software work during the 2024 comparison. It was particularly compelling for code review, debugging, refactoring, repository explanation and preserving the intent of an existing codebase. Its 200,000-token context window was also useful when a task required supplying many files or documents at once.
Anthropic reported the updated Claude 3.5 Sonnet at 49.0% on SWE-bench Verified in its October 2024 computer-use announcement. A later Claude 3.5 Sonnet result reported in the same general comparison was 40.6%. These numbers should not be treated as interchangeable: they refer to different versions and evaluation setups. The relevant announcement is Anthropic’s updated-model and computer-use release.
Agent benchmarks also complicate the story. In OpenAI’s MLE-Bench results for the AIDE machine-learning engineering task, the listed scores were:
- GPT-4o 2024-08-06: 19.70%.
- Claude 3.5 Sonnet 2024-06-20: 18.55%.
This is evidence from one task family and one agent framework, not proof that GPT-4o was the better coding model overall. Agent results can depend heavily on repository setup, tool access, patch-generation loops and the test harness. The figures are documented in the MLE-Bench repository.
| Coding task | Likely 2024 advantage | Important qualification |
|---|---|---|
| Greenfield code generation | Claude, slight or variable | Language, prompt and framework matter. |
| Debugging existing code | Claude was often preferred | User preference is not controlled benchmark evidence. |
| Repository-scale work | Claude’s larger context was useful | Nominal context length does not guarantee comprehension. |
| Fast snippets and prototypes | GPT-4o was competitive | Tool and product integration may matter more than a score. |
| Tool-using agents | No universal winner | The agent scaffold can dominate the result. |
| Code explanation | Rough parity | Judge correctness separately from readability. |
Practical verdict: choose Claude 3.5 Sonnet for the historical text-and-code comparison when careful refactoring, debugging and repository understanding are the priority. Choose GPT-4o when fast interaction, OpenAI tooling or multimodal development workflows are more important. For a production system, test the currently available successor models rather than assuming a legacy benchmark still predicts today’s result.
Writing, editing and instruction following
Claude 3.5 Sonnet was widely regarded as especially strong at long-form prose, nuanced rewriting, technical documentation and following complex written instructions. It often made a good choice when the task required maintaining tone, preserving constraints and working through a large amount of text.
GPT-4o remained highly capable for writing and could be more convenient when drafting was part of an interactive ChatGPT workflow involving images, files, voice or other OpenAI features. The difference was not that one model could write and the other could not; it was usually a combination of prose preference, instruction adherence, editing behavior and product context.
Claims such as “Claude is more human” or “GPT-4o is more creative” are subjective unless supported by a blind evaluation. A fair writing test should give both systems the same source material and instructions, hide the model names from reviewers, and score:
- Factual preservation.
- Completeness.
- Structure and clarity.
- Adherence to tone and formatting requirements.
- Unnecessary changes or unsupported additions.
For high-stakes writing, factual accuracy and instruction compliance should be scored separately from style. A fluent answer can still contain errors.
Multimodal and vision performance
GPT-4o’s most important advantage was not a single text benchmark. It was the breadth of its native multimodal design and its integration into ChatGPT. OpenAI positioned GPT-4o for text, image, audio and speech interaction, including real-time conversational capabilities. Its launch announcement and system card describe the relevant capabilities and evaluation work.
“Multimodal” covers several different abilities:
- Understanding an uploaded image.
- Reading documents and OCR text.
- Interpreting charts, diagrams and screenshots.
- Processing audio.
- Holding a speech-to-speech conversation.
- Understanding video or sequential visual information.
- Generating images, which is a separate capability from image understanding.
Claude 3.5 Sonnet supported text and vision input and was strong for document and visual analysis, but it did not offer the same integrated voice and real-time interaction story as GPT-4o. That does not mean GPT-4o was better at every visual task. Independent vision studies measure particular datasets and abilities, not the complete consumer experience. One example is the task-specific GPT-4o vision evaluation.
Choose GPT-4o in the historical comparison when the workflow combines speech, images and text or depends on ChatGPT’s integrated multimodal experience. Choose Claude when the “multimodal” task is primarily document, screenshot or image analysis inside a text-heavy workflow.
Long documents and context windows
Claude 3.5 Sonnet launched with a nominal 200,000-token context window, compared with the commonly documented 128,000-token context window for GPT-4o. That gave Claude an obvious advantage for large documents, long codebases and multi-file analysis.
However, a larger context window is not automatically a better long-context system. The important questions are:
- Can the model retrieve facts near the beginning, middle and end?
- Can it reconcile contradictory documents?
- Does accuracy degrade as irrelevant material accumulates?
- Can it navigate a large codebase instead of merely summarizing it?
- Does it preserve citations and source locations?
- Is the application able to send and manage that much context affordably?
Very large prompts can increase cost and distraction. Effective context—the amount of information a model can use accurately—is not the same as the advertised maximum. For long-document work, test retrieval and synthesis at the actual prompt sizes your application will use.
What’s actually slowing this PC down?
Pick the symptom - the matching free tool is one click away.
Speed and latency
GPT-4o was introduced as faster than earlier GPT-4-class systems, and OpenAI described a substantial price reduction compared with GPT-4 Turbo. That made it attractive for interactive applications, but it would be inaccurate to declare one model categorically faster without specifying the measurement.
Useful latency measurements include:
- Time to first token.
- Tokens generated per second.
- Total response time.
- Prompt and output length.
- Streaming behavior.
- API region and service tier.
- Consumer-app queueing and rate limits.
Consumer-app responsiveness is not the same as API throughput. A short ChatGPT answer and a long streamed API response should not be compared as if they were identical tests.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.API cost: Claude was cheaper on input at launch
At launch-era API pricing, GPT-4o cost $5 per million input tokens and $15 per million output tokens. Claude 3.5 Sonnet cost $3 per million input tokens and $15 per million output tokens.
That made Claude 3.5 Sonnet cheaper for input-heavy workloads such as large-document analysis, repeated codebase prompts and retrieval-augmented applications. Output pricing was equal in the cited launch comparison. The saving mattered most at scale, but only while the relevant endpoint was available at those rates.
Do not confuse API pricing with consumer subscriptions. ChatGPT Plus or Pro and Claude Pro or Team are subscription products with their own limits, features and regional availability. Token prices apply to developer APIs, not automatically to the consumer apps.
Best Value
As of 2026, both models are legacy products. Check the live OpenAI model page and Anthropic pricing documentation before selecting an endpoint. A historical price advantage is not useful if the model is deprecated, unavailable in your region or unsuitable for a new integration.
Reliability, safety and hallucinations
Raw intelligence scores do not answer whether a model is reliable in production. Evaluate separately:
- Factual error rate.
- Unsupported citations and fabricated references.
- Overconfidence and uncertainty handling.
- Refusal behavior.
- Prompt-injection resistance.
- Tool-use safety.
- Privacy, logging and data-retention controls.
- Enterprise governance and access management.
Neither model should be described as categorically safer or more accurate without a defined dataset, policy version and deployment setup. Behavior can change between the API, consumer product, system prompt and connected tools. OpenAI’s GPT-4o system card provides an example of the type of safety and risk documentation to examine.
Do these 3 things before closing this tab:
1Fix the driver behind crashes, sound loss and screen glitches2Clear out junk files and repair common Windows errors3Scan for outdated or missing drivers - takes under a minuteFor high-stakes medical, legal, financial or operational tasks, require human review and verify important claims against authoritative sources regardless of which model is used.
Which model should you choose?
| Use case | Better historical fit | Why |
|---|---|---|
| Coding, debugging and refactoring | Claude 3.5 Sonnet | Strong text-coding results, instruction following and long-context workflows. |
| Long-form writing and editing | Claude 3.5 Sonnet | Often preferred for nuanced prose and constraint-heavy editing. |
| Voice and real-time conversation | GPT-4o | Native audio and speech capabilities were a major product distinction. |
| Image, audio and text in one workflow | GPT-4o | Broader integrated multimodal product experience. |
| Large documents and codebases | Claude 3.5 Sonnet | Its 200,000-token launch context window was larger. |
| Input-heavy API workloads | Claude 3.5 Sonnet at launch | $3 per million input tokens versus GPT-4o’s $5, subject to historical availability. |
| OpenAI-specific integrations | GPT-4o | Existing ChatGPT and OpenAI infrastructure may reduce integration work. |
| New production deployment in 2026 | Neither by default | Evaluate currently supported successor models and lock a dated snapshot where possible. |
If you are comparing them for a real project, run a small evaluation using your own prompts and documents. Include representative coding tasks, failure cases, long-context retrieval, structured output, tool calls and cost. Keep the model snapshot, system prompt, temperature, tools and evaluator fixed so that the result remains reproducible.
Are GPT-4o and Claude 3.5 Sonnet still worth using in 2026?
Only if your platform still provides the exact model and your workflow benefits from its known behavior. OpenAI’s current GPT-4o documentation recommends newer models for most API integrations. Anthropic’s current pricing documentation lists Claude 3.5 Sonnet as deprecated. Access can still vary between direct APIs, cloud marketplaces, archived snapshots and consumer products.
For a new application, do not select either model solely because an older comparison says it won a benchmark. Check:
Recommended Free Tools
- Whether the exact snapshot is currently available.
- Its retirement and migration policy.
- Current input and output prices.
- Supported modalities and tools.
- Rate limits and regional availability.
- Structured-output and function-calling behavior.
- Privacy and enterprise requirements.
- Performance on your own evaluation set.
Developers may also encounter these models indirectly through coding and cloud platforms. Tools such as GitHub Copilot, Cursor, Windsurf, Amazon Bedrock and Google Cloud Vertex AI can add repository indexing, tool execution, routing, governance and context management. The advertised model name may not guarantee that every request uses the same historical snapshot.
Final verdict
Claude 3.5 Sonnet was usually the better text-and-code specialist: it had a strong showing on several reasoning and coding benchmarks, a larger launch context window and a lower launch-era input-token price. GPT-4o was the more versatile multimodal product, particularly for integrated voice, speech, image and text interaction inside ChatGPT.
So the answer depends on the job. Choose the Claude side of the historical comparison for long-form writing, code review and large text-heavy workflows; choose GPT-4o for real-time multimodal interaction and OpenAI-centric tooling. For a decision made in 2026, treat both as legacy models and test their currently available successors instead of treating the old benchmark winner as an automatic recommendation.
Quick Recap
Product prices and availability are accurate as of the date/time indicated and are subject to change. Any price and availability information displayed on Amazon at the time of purchase will apply.
