GPT-4, Claude 3.5, and Gemini 1.5 were leading model families in 2024 and 2025, but they are now legacy comparison points rather than sensible defaults for a new build. Use this comparison to understand their design tradeoffs, then benchmark currently supported models on your own workload before choosing a provider.
Why this GPT-4 vs Claude 3.5 vs Gemini 1.5 comparison needs context
Model catalogs change faster than application architecture. OpenAI retired GPT-4o from ChatGPT in February 2026, while noting that the API was unaffected at the time of that announcement. Google's current Gemini deprecation schedule lists newer Gemini generations and replacement paths. A static 2025 price or context-window table would now mislead.
The original three families still illustrate enduring choices. OpenAI emphasized broad API tooling and multimodal interaction. Claude 3.5 was widely evaluated for coding and long-document work. Gemini 1.5 drew attention to very large context and multimodal inputs. Those descriptions do not establish which current model will perform best in your application.
Availability also differs between a consumer chat product and an API. The OpenAI GPT-4o retirement notice explicitly separated the ChatGPT change from API availability. Always check the endpoint, region, cloud platform, and dated model identifier you plan to deploy.
Compare LLMs by workload, not brand
An LLM model comparison should begin with representative tasks. Create examples from real inputs, including difficult cases and expected failures. Run the same prompt, tools, retrieval context, output schema, and scoring rubric against every candidate.
- Task success: Did the model complete the business outcome rather than merely produce fluent text?
- Instruction following: Did it respect format, scope, and policy constraints?
- Grounded accuracy: Did claims follow the supplied documents and tool results?
- Tool use: Were functions selected correctly with valid arguments?
- Latency: Compare median and tail response times under realistic concurrency.
- Total cost: Include input, output, caching, tool calls, retries, and engineering overhead.
Separate quality by task slice. A model that leads on code generation may underperform on extraction from noisy invoices. Another may handle long context well but cost too much for high-volume classification. One average score cannot represent all product paths.
Context window size is not context quality
Gemini 1.5 made long context a central comparison point, but advertised token capacity is only a limit. Teams still need to test how reliably a model finds information buried in the middle, distinguishes similar passages, follows recent instructions, and maintains acceptable latency as input grows.
Large prompts can also increase cost and expose unnecessary data. Retrieval, document selection, and summarization often produce better control than sending an entire repository or archive on every request. Measure answer quality at several context lengths and record which source passages the response actually used.
Tool calling and structured output shape the application
For an agent, the model is one component in a controlled workflow. Test schema validity, tool selection, argument grounding, retry behavior, and recovery from tool errors. A provider may support the needed feature, yet a specific model version may behave differently on nested schemas or ambiguous tool choices.
Do not allow model output to execute privileged actions without validation. Application code should enforce authorization, input constraints, idempotency, spending limits, and approvals. The model proposes; trusted services decide what can run.
Teams building provider-neutral products should place these controls inside an AI integration layer. That boundary can normalize request options, record model versions, and route tasks without leaking provider-specific response shapes throughout the codebase.
Price comparison needs a dated usage model
Vendor prices, token accounting, cache discounts, batch rates, and regional hosting options can change. Record the date and source for every estimate. Then calculate cost from a trace of real requests rather than multiplying one advertised rate by an assumed token count.
Include prompt tokens, retrieved context, output length, retries, evaluation calls, and fallbacks. A cheaper model that fails more often may cost more per successful task. A larger model used only for difficult requests can be cheaper than sending every request to it.
Migration risk belongs in the decision
The 2025 model names demonstrate why applications should expect deprecation. Pin dated model versions where the provider supports it, monitor retirement notices, and keep an evaluation suite ready for replacements. Avoid abstractions that erase useful provider features, but isolate credentials, request construction, error mapping, and model selection behind a narrow interface.
A fallback provider helps only when it has been tested. Different tokenizers, safety behavior, tool schemas, and prompt interpretation can turn an untested failover into a second incident. Run the fallback on the same regression suite and rehearse switching under controlled conditions.
A repeatable model selection process
To choose an LLM for production, treat provider claims as hypotheses and require workload evidence.
- List currently supported candidates. Confirm API availability, region, data handling, and announced shutdown dates.
- Freeze a representative dataset. Include normal tasks, costly failures, safety cases, and long-context examples.
- Define thresholds before testing. Set minimum task success and maximum latency, cost, and policy-failure rates.
- Run blinded comparisons. Hide provider names from human reviewers and randomize response order.
- Inspect failures by category. Read traces for retrieval, reasoning, formatting, and tool errors.
- Pilot with production safeguards. Limit traffic, monitor drift, and preserve a rollback path.
- Schedule reevaluation. Re-run the suite when prompts, tools, data, or model versions change.
If the team lacks a stable evaluation harness, an AI developer for model integration should first build the tests, telemetry, and provider boundary. The correct choice is not the model with the strongest old reputation. It is the supported model that meets today's task-specific thresholds and can be replaced without rewriting the product.