The GPT-5 release and the buzz around it haven’t settled down yet. The model isn’t as obscurely expensive as GPT-4.5. It delivers and stands above the competition. And it’s a welcome addition to the GPT model family. But many now wonder how it compares to various now-discontinued OpenAI models. We’re talking about GPT-4.1, 4.5, o3, etc. We’ve already compared GPT-5 with the Claude 4 model family, and now it’s GPT-4’s and o3’s turn. Here’s a detailed GPT-5 vs GPT-4 vs o3 analysis focusing on how each performs in coding. Let’s get started.
GPT-5 vs GPT-4 vs o3: Quick cheat-sheet
- GPT-5 — best for deep, multi-file code reasoning, multimodal debugging (screenshots/diagrams), and high-confidence fixes; available in “mini/nano/thinking” variants with cost/latency tradeoffs. As good as the Claude 4, which you can try here.
- o3 (o-series) — OpenAI’s reasoning-first family (o3, o3-mini, o3-pro); tuned for math, logic, and technical correctness at good cost/perf points. Great when you need rigorous step-by-step inference. Discontinued. Although you can try it here.
- GPT-4 — still solid for everyday drafting, brainstorming, and lighter coding help; simpler UX and broadly compatible. Use it when you don’t need heavy cross-file inference.
GPT-5 Model Family Overview and Comparison with GPT-4 and o3
OpenAI is marketing GPT-5 as a family of models and an intelligent router that picks the right variant for each request. It ships with “general” and “thinking” variants and claims significant gains on reasoning and coding benchmarks compared to GPT-4. Official documentation highlights improved scores on math, coding, and multimodal tasks, expectedly.
Compared to GPT-4o, 4.1, and o3, GPT-5 improves upon reasoning, coding accuracy, and handling multimodal inputs like text, images, and more. Official tests show higher scores in math, programming, and cross-modal problem-solving. For people who depend on AI not just to be creative, but also to be consistently right, GPT-5 offers a blend of adaptability, precision, and speed that makes it feel less like a tool and more like a capable partner.
You can try GPT-4o, 4.1, and o3 here.
GPT-5 Benchmarks and Empirical Performance
Benchmarks matter. Here’s how the numbers compare:
- OpenAI reports that GPT-5 scores 74.9% on SWE-bench Verified and 88% on Aider Polyglot for coding tasks.
- GPT-4.1: 54.6% on SWE-bench Verified and 76.9% on Aider Polyglot.
- GPT-4.5: 38% on SWE-bench Verified and 45% on Aider Polyglot.
- o3: 69.1%–71.7% on SWE-bench Verified and 78.2%–83% on Aider Polyglot, depending on the specific version and testing methodology.
Independent benchmarks and developer reviews broadly confirm a meaningful uplift in many real-world coding scenarios, although not uniformly across every workload. Some community tests show exceptional gains when “thinking”/chain-of-thought settings are enabled; others find edge-cases and style regressions compared to specialized competitors.
GPT-5 vs GPT-4 vs o3: Context Window Comparison
Key highlights:
- GPT-4.1 leads by a massive margin with a 1,000,000-token context window, making it ideal for handling extremely large inputs.
- GPT-5 supports 256,000 tokens (25.6%), offering a balance between scale and performance.
- o3 follows with 200,000 tokens (20%).
- Both GPT-4o and GPT-4.5 have 128,000 tokens (12.8%).
What feels different while coding?
Speed and responsiveness: GPT-5 is designed to be faster for common tasks. For many small, iterative queries—refactors, short bug fixes, or writing unit tests—responses are snappy. In practice, that reduces friction during pair-programming sessions and keeps context alive across interactions.
Quality of generated code: Code from GPT-5 tends to be more idiomatic and better at multi-file reasoning. Where GPT-4 sometimes produced plausible but brittle code, GPT-5 reduces hallucinated APIs and mismatched types in typical stacks. That said, reviewers report variability: for UI-heavy tasks or detailed UX implementations, GPT-5 can still produce thin skeletons that need human polish.
Debugging and reasoning about complex codebases
This is where GPT-5 shines relative to GPT-4. The “thinking” mode and improved context handling help with tracing bugs across files, suggesting fixes that respect hidden invariants, and summarizing long diffs with fewer follow-ups. For code review and PR summarization, third-party benchmarks and internal tests show higher quality on typical enterprise PR tasks. However, the gains are not a magic wand: GPT-5 still approximates likely fixes and can miss domain-specific intentions. Use it to triage and propose patches faster, but keep the human in the loop for domain validation.
Multi-modal help: reading screenshots, diagrams, and docs
GPT-5’s multimodal abilities are better than GPT-4’s older capabilities. That means it handles screenshots of stack traces, diagrams, and mixed text+image PRs more reliably. For frontend developers, feeding a screenshot of a broken component plus the associated CSS file gets more accurate diagnoses than GPT-4 did in similar tests. This matters for debugging UI regressions and rapidly understanding legacy code.
Tooling and API changes that matter to devs
OpenAI shipped developer-focused pages describing GPT-5 for developers and integrations across platforms (GitHub Models, API variants). The router that automatically selects a model variant means less manual experimentation but more need to understand pricing and rate limits for each variant. GitHub Models support makes it easier to use GPT-5 directly in code hosts, improving workflows like autocompletion, PR generation, and in-CI checks.
If you build on OpenAI’s API, the new model family and thinking variants give you knobs—faster, cheaper “mini” models and a higher-latency “thinking” model that does heavier reasoning.
GPT-5 vs GPT-4 vs o3: Pricing Comparison
GPT-5 is dramatically less expensive than GPT-4.5, setting a new pricing benchmark for advanced AI models. API rates for standard GPT-5 are $1.25 per million input tokens and $10 per million output tokens—that’s nearly 60 times cheaper for input and 15 times cheaper for output compared to GPT-4.5, which costs $75 per million input tokens and $150 per million output tokens. This massive drop makes GPT-5 accessible for projects that previously would have been cost-prohibitive with 4.5.
The Mini and Nano tiers take savings even further, with GPT-5 Mini at $0.25 input / $2 output and GPT-5 Nano at just $0.05 input / $0.40 output per million tokens. These discount tiers make high-volume and cost-sensitive deployments exceptionally affordable—up to 25 times less than GPT-4.5 and still lower than the fastest o3 models.
For reference, the o3 model now bills at $2 per million input tokens and $8 per million output tokens after a recent 80% price cut, making it much cheaper than its past rates but still more expensive than GPT-5 and far pricier than GPT-5 Mini or Nano for most workloads.
Subscription plan limits for individual users and small teams remain stable, with both GPT-5 and GPT-4.5 offering paid options from $20/month and similar free usage caps. However, the new pricing unlocks GPT-5’s higher speed, better reasoning, and fewer hallucinations, while enterprise users benefit most from the vastly cheaper and more granular token-based tiers introduced with GPT-5.
GPT-5 vs GPT-4 vs o3: Security, Hallucinations, and Safety
GPT-5 reduces some hallucination classes compared to GPT-4, particularly around API usage and type errors, but it does not eliminate them. The model’s improved confidence calibration helps, yet you must still rely on tests, static analyzers, and linters. For security-sensitive code (auth, crypto, cryptographic protocols), treat outputs as draft proposals — never deploy without rigorous review. Organizations with compliance needs should audit the model’s outputs and establish guardrails.
Developer workflows that change
Pair programming: GPT-5 works better as a real-time pair, making suggestions that fit the current codebase and catching more subtle mistakes.
Code review: Use GPT-5 to generate first-draft PR descriptions, summarize diffs, and propose focused test cases. It accelerates reviewers by pointing out likely edge cases.
Refactoring: Larger-scope refactors are easier because GPT-5 holds more context and reasons across files.
Prototyping: Rapid prototypes and scaffolding are faster, but you’ll still hand-polish complex UIs.
CI integration: Treat GPT-5 as an advisory check (generate tests, flag risky changes) rather than an authoritative gate; combine with existing test suites.
GPT-5 vs GPT-4 vs o3: Reddit Perception
Reddit reactions are split and nuanced. Pockets of excitement celebrate GPT-5’s code review chops and PR summarization; others note that for complex product design, the model still needs tight prompts and lots of iteration. Some threads claim GPT-5 outperforms Anthropic’s Opus or Sonnet in certain tasks, while others show the opposite for specific workloads. A consistent theme: GPT-5 often speeds the mechanical parts of development, but product judgment and final architectural choices remain human.
- “Fast and good at fixing” — A user reporting strong bugfix and iteration performance but needing additional refinement.
- “Quality is spotty for UI” — frontend devs reporting skeletal outputs that require polishing.
- “Benchmarks show gains, but real projects vary” — devs pointing to SWE-bench and PR benchmarks yet cautioning about edge cases.
Concrete examples: prompts and outcomes
To make this less abstract, here are three real-world mini-scenarios where GPT-5 shows a clear advantage over GPT-4.
- Cross-file bug trace: Give GPT-5 the failing test, the stack trace screenshot, and the two implicated files. GPT-5’s multimodal understanding and “thinking” variant can propose a focused patch that changes a handful of lines in the right file and proposes a unit test. GPT-4 would often require more back-and-forth to locate the root cause.
- PR summarization at scale: For a 500-line diff touching services and frontend components, GPT-5 generates a prioritized bullet list of functional changes, possible regressions, and suggested test cases—useful for busy reviewers. GPT-4 gave plausible summaries but tended to miss cross-service side effects more frequently.
- Generating integration tests: Feed GPT-5 a public API spec and corresponding DB migration. It can scaffold integration tests with setup/teardown and common failure cases. GPT-4 was capable but generated more brittle fixtures that needed human hardening.
Prompt templates that work well
- Context first, ask second: start with the repo link (or pasted code), then the failing test or desired behavior, then the exact deliverable (“Write a pytest that demonstrates the bug and a minimal fix”).
- Limit scope: when requesting refactors, ask for changes only in specific modules to avoid sprawling edits.
- Safety net: always append “include unit tests and run with pytest; mark assumptions explicitly” to force testable output.
GPT-5 vs GPT-4 vs o3 Cost-benefit Math
Example assumptions: a team of 10 engineers spends 4 hours/week on PR triage + repetitive fixes. GPT-5 saves 25% of that time. Step by step:
- 25% of 4 hours = 1 hour/week saved per engineer.
- 10 engineers → 10 hours/week saved.
- At $60/hr fully-loaded → $600/week saved.
- 52 weeks → $31,200/year saved.
- If GPT-5 costs $2,000/month extra for integrations / higher tiers → $24,000/year cost.
Net ≈ $7,200/year benefit in this illustrative example.
Your actual numbers will differ — measure before you buy.
Human + AI roles
- Humans retain strategic authority: architecture, product decisions, security tradeoffs.
- AI handles routine cognitive load: boilerplate, tests, code comments.
- Humans enforce correctness: tests, manual review, and domain validation.
GPT-5 vs GPT-4 vs o3 – Try these Prompts!
Try these complex coding and general-purpose prompts to compare both models:
The Bottom Line
Upgrade if you: run large codebases, need better PR triage, frequently debug tricky cross-file bugs, or want the fastest gains in developer productivity and are prepared to pay more.
Hold off if you: are a hobbyist, have very tight budgets, or your primary workload is pixel-perfect UI design where model outputs require heavy human polishing.
Try a targeted pilot. Measure time saved and error rates. If GPT-5’s improvements map to your pain points—especially PR work, complex bug triage, and multi-file refactors—upgrade. If your needs are cheaper autocompletion or occasional brainstorming, GPT-4 still serves very well.
And… if you’re wondering if it’s possible to use older (allegedly discontinued) OpenAI models, the answer is yes. You can try them on Bind AI here.