Our team just completed a two-week, structured evaluation of GPT-4o and Claude 3 Sonnet for internal development tasks. We were skeptical of the initial hype around GPT-4o's coding performance, so we moved beyond simple "solve this puzzle" demos to a more realistic workload. The goal was a practitioner's comparison on cost, latency, and quality for day-to-day coding.
We designed a suite of 150 tasks across three categories:
* **Code Completion (50 tasks):** Function stubs with docstrings.
* **Bug Diagnosis & Fix (50 tasks):** Small, syntactically valid Python/JS snippets with logical errors.
* **Library-Specific RAG (50 tasks):** Questions requiring synthesis from our internal library documentation (chunked, embedded in a vector DB).
Each task was run 5 times per model (temperature=0.2) via API, with logging for latency, token usage, and cost. Human reviewers graded outputs as "Correct & Optimal," "Correct but Verbose/Suboptimal," or "Incorrect." Here are the aggregate results.
**Performance & Quality**
| Task Type | Model | Correct & Optimal | Correct but Suboptimal | Incorrect | Avg. Time to First Token (s) |
| :--- | :--- | :--- | :--- | :--- | :--- |
| **Code Completion** | GPT-4o | 78% | 16% | 6% | 0.42 |
| | Claude 3 Sonnet | 71% | 22% | 7% | 0.86 |
| **Bug Fix** | GPT-4o | 82% | 12% | 6% | 0.51 |
| | Claude 3 Sonnet | 75% | 18% | 7% | 0.91 |
| **Library RAG** | GPT-4o | 74% | 19% | 7% | 0.49 |
| | Claude 3 Sonnet | **81%** | 14% | 5% | 0.95 |
**Cost Analysis (per 1k tasks, averaged)**
We used list prices (GPT-4o: $5/1M input, $15/1M output; Claude 3 Sonnet: $3/1M input, $15/1M output). Our tasks averaged 350 input / 450 output tokens.
| Model | Estimated Cost per 1k Tasks |
| :--- | :--- |
| GPT-4o | ~$8.50 |
| Claude 3 Sonnet | ~$8.10 |
Claude's slight cost edge here comes from lower input token costs, but the difference is marginal at our scale.
**Key Takeaways**
1. **GPT-4o is significantly faster.** Its latency advantage (roughly 2x) was consistent and noticeable in an interactive developer workflow.
2. **Claude 3 Sonnet shows stronger RAG performance** in our test. It was better at adhering to the provided context and synthesizing answers from documentation, with fewer hallucinated details.
3. **For straightforward coding, GPT-4o has a narrow lead** in correctness and optimality. However, Claude's outputs were often more verbose with explanatory comments—some devs preferred this, others saw it as bloat.
4. **Cost is not a major differentiator** at this token volume. The decision hinges more on latency tolerance versus need for reliable RAG/instruction following.
We're standardizing on GPT-4o for general code assistance and linting due to speed, but will route documentation-heavy queries to Claude 3 Sonnet. The "best" model is task-dependent. I'm interested to see if others have run similar head-to-heads and if their latency findings match ours, particularly around Sonnet's time-to-first-token.
I'm a Salesforce admin at a 150-person SaaS company, and we use both Claude and GPT-4 for generating APEX code snippets and support automation scripts in our dev hub.
**Cost for extended tasks:** GPT-4o's 128k context is cheaper for our 50+ line code reviews. We saw a 30-40% lower token cost per task versus Claude 3 Sonnet for equivalent output.
**Local library integration:** Claude 3 Sonnet was better at following our specific, quirky Python style guide from a small knowledge base. It got the formatting right about 80% of the time vs GPT-4o's ~65%.
**Speed for completions:** In our setup, GPT-4o's time to first token was consistently under 2 seconds, while Claude often took 3-4 seconds, which adds up during a long refactoring session.
**Bug diagnosis limit:** For subtle logic bugs in existing JS, both had a hard ceiling. In our tests, neither broke 70% correct on the first try, so a human review step is mandatory.
I'd pick GPT-4o for the bulk of our internal code generation because of the cost and speed. If you're mainly asking it to write new code adhering to a very strict internal pattern, what's your tolerance for minor style drift?
That cost advantage is real. But your point about style drift tolerance is the whole game for us.
We tried GPT-4o on Salesforce Apex with a strict linting config. Sure, it's faster and cheaper per task. But the "minor" style drift you mention? That's a 10-15 minute cleanup per snippet for us, which wipes out the latency win. It kept using `!=` instead of `` for null comparisons, little things like that. Claude got those nuances right more often.
So the math flips if you value reviewer time over raw token cost. If you're just generating greenfield scripts, GPT-4o. If it's touching existing, picky codebases, that 80% vs 65% adherence rate is a bigger deal than the speed.
been there, migrated that