GrowthX
Learn: AI Watch
October 19, 2025

Which AI Model to Choose? October 2025 Selection Guide

Cover Image

Someone dropped this in our engineering Slack: "Does anyone have a good guide for which models to use when? Like, I'm scanning a web page and want to get a summary, classify it against our taxonomy—how do you actually choose? Or should I just ask ChatGPT to help me pick?"

If you're asking the same question, you're not alone. We're all making model choices based on gut feelings, outdated benchmarks, or whatever we used last time.

This guide is based on a mix of current benchmark data, official model documentation, and our own experience at GrowthX where we process ~10 billion tokens per month across Anthropic and OpenAI combined.

Our take: At GrowthX, we constantly switch models as the landscape shifts. Benchmarks don't tell the full story—real-world performance varies dramatically by use case. Claude 4.5 Sonnet dominates our SVG generation workflows and excels in all our agents using llm-as-a-judge, interpreting ambiguous feedback and exiting loops faster. It also has better "taste" for writing quality. GPT-5 with Codex, on the flip side, excels at generating polished designs with Shadcn/Tailwind—it has real "taste" for UI design. With Claude 4.5 Haiku's launch, we're testing the switch to Anthropic for more workflows where speed and cost matter.

Why Haiku 4.5 might change the game: What was frontier performance five months ago (Claude Sonnet 4) is now available at 1/3 the cost and 2x the speed. With 73.3% on SWE-bench at $1/$5 per million tokens, it beats GPT-4o (30.8%) and approaches Claude 4.5 Sonnet (77.2%) and GPT-5 (74.9%) at a fraction of the latency and price.

Quick Decision Guide

Skip the details? Here's the TL;DR:

Your Use CaseRecommended ModelWhy
High-volume, cost-sensitive tasksGPT-4o-mini$0.15/$0.60 per 1M tokens crushes competition on price
API code generation (simple)Claude 4.5 Haiku$1/$5 per 1M tokens, 73.3% SWE-bench, blazing fast
API code generation (complex)Claude 4.5 Sonnet77.2% SWE-bench (82% with test-time compute)—highest score available
Agentic workflows & automationClaude 4.5 Sonnet#1 for command-line, GUI automation, multi-tool orchestration
Real-time chatbotsClaude 4.5 HaikuNear-frontier performance, 2-4x faster than Sonnet
Structured output/JSONGPT-5100% schema compliance guaranteed
Large document processingClaude 4.5 Sonnet200K standard, 1M beta context window
Batch processingGPT-4o-mini or Claude 4.5 SonnetBoth offer 50% batch discounts

Critical gotchas:

Here's what you actually need to know as of October 2025:

The Model Landscape: What You're Choosing From

Late 2025 gives you a handful of production-ready models through APIs. Each has distinct strengths and costs. You're choosing between proven options with established benchmarks.

What's available:

  • OpenAI: GPT-5 ($10.00 input / $30.00 output per million tokens), GPT-4o ($2.50 input / $10.00 output per million tokens), GPT-4o-mini ($0.15 input / $0.60 output per million tokens)
  • Anthropic Claude: Claude 4.5 Sonnet ($3.00 input / $15.00 output per million tokens), Claude 3.5 Sonnet and Haiku variants
  • Google Gemini: Competitive pricing with strong multimodal capabilities
  • Open-source alternatives: Increasingly viable for specific use cases

The tradeoff that matters:

Every model selection boils down to cost versus performance versus speed.

But here's what changed in 2025: according to Stanford HAI, top-tier models now perform within 0.7% of each other on standard benchmarks. Down from 4.9% in 2023.

This convergence means implementation factors matter more than marginal performance differences.

Stop obsessing over which model scores 2% higher on standardized tests. Focus on pricing, rate limits, API reliability, and how well each model integrates with your existing infrastructure.

Performance Deep Dive: What These Models Can Actually Do

Benchmarks tell you one story. Production tells you another.

Code Generation & Technical Tasks

Researchers test coding ability by having models fix real bugs and add features from actual GitHub projects using SWE-bench Verified. Here's what the numbers say:

But here's where it gets interesting.

A controlled study by METR research found that experienced developers saw a 19% productivity slowdown when using early-2025 AI tools. This study has been widely shared, but we strongly disagree with its conclusions.

Our experience: Developer productivity with AI is tightly related to how well engineers understand LLMs and their tooling of choice (Claude Code, Cursor, Codex, etc). Within our own organization, the delta between engineers ramping up on the LLM stack (prompt engineering, deep understanding of tooling like creating sub-agents in Claude Code, etc.) versus fully ramped engineers is multiples of productivity—not marginal gains. This is another example where papers and benchmarks don't paint the full picture at all.

The reality: Benchmarks tell one story. Your workflow and expertise tell another.

Use Case 1: AI Coding Assistants (Claude Code, Cursor, Copilot)

You're choosing a model to power your IDE assistant—the thing that's writing code alongside you, managing files, running commands.

Winner: Claude 3.5 Sonnet

Why? It's the most admired model among developers (51.2% preference) despite not leading on benchmarks. It excels at agentic coding and tool orchestration—the stuff that matters when you're actually building features.

Why not GPT-5? It wins on raw benchmark scores but struggles with variable latency ("extremely slow compared to 4.1 or 4o") and prompt sensitivity issues (simple prompts flagged as policy violations).

For an IDE assistant, responsiveness and reliability beat raw performance.

Use Case 2: API-Based Code Generation

You're calling an LLM API to generate code programmatically—autocomplete, code review, test generation, refactoring suggestions.

For routine completion and debugging:

  • Claude 4.5 Haiku: 73.3% SWE-bench at $1/$5 per 1M tokens—near-frontier performance, blazing speed
  • GPT-4o-mini: Still viable at $0.15/$0.60 if you need rock-bottom pricing

For complex refactoring or architectural work:

  • GPT-5: 74.9% SWE-bench score (slightly edges Haiku)—if you can tolerate the latency
  • Claude 4.5 Haiku: 73.3% score with 2-4x faster responses—better choice for most teams
  • Warning: GPT-5 has no specific TTFT measurements available, reports of extreme slowness

For multi-step feature building:

  • Claude 4.5 Sonnet: Best for complex tasks with 77.2% SWE-bench score, excels at tool orchestration
  • Claude 4.5 Haiku: Good for sub-agent orchestration at much lower cost

Language-specific considerations:

  • JavaScript/TypeScript: Strong across all models, minimal differentiation
  • Python data science: Claude variants shine
  • Systems languages (C++/Rust): GPT models give more detailed debugging explanations

Reasoning & Problem Solving

Headline benchmark numbers: modern models achieve impressive scores on mathematical reasoning tasks.

The practical story: more nuanced.

Multi-step problem solving depends on how you structure your requests:

  • Prompt structure: How you organize instructions impacts reasoning quality
  • Information management: What you feed the model affects consistency
  • Chain-of-thought reasoning: Now standard across major models
  • Consistency versus peak performance: Some models excel at peak but struggle with reliability

That trade-off between consistency and peak capability? It matters more than absolute performance numbers in production.

Text Processing & Understanding

For summarization, classification, and extraction—the bread-and-butter tasks most applications need—the model landscape has commoditized. Performance differences for typical document processing are minimal across major providers.

But implementation factors determine your actual costs and performance. Here's what matters:

For batch document processing:

  • Best batch discount: Both OpenAI and Anthropic offer 50% discounts for non-real-time processing
  • Cost at scale (10K documents/day, 500 input + 200 output tokens):
    • GPT-4o-mini: $40/month standard, $20/month batch
    • Claude 4.5 Sonnet: $450/month standard, $225/month batch
    • GPT-4o: $325/month standard, $162.50/month batch

For large context processing:

  • Context window leader: Claude 4.5 Sonnet (200K standard, 1M beta for tier 4+)
  • OpenAI models: 128K across GPT-5, GPT-4o, GPT-4o-mini
  • Practical caveat: "Lost in the middle" problem—information buried in large contexts gets ignored

For structured output reliability:

For speed:

  • GPT-4o-mini: Consistently fast
  • Claude 3.5 Haiku: 0.36s TTFT if speed is critical

Bottom line for text processing: Use GPT-4o-mini for cost-sensitive volume work. Use Claude 4.5 Sonnet when you need large context windows or prompt caching benefits.

Structured Output & Function Calling

Structured output: getting the model to return data in a specific format like JSON that your application can reliably process.

Function calling: having the model automatically use tools or APIs based on conversation context.

Newer models promise improved capabilities. Reality depends on your specific schema requirements.

Simple schemas work reliably across all models:

{ "sentiment": "positive", "confidence": 0.95, "categories": ["product", "feedback"] }

Complex schemas expose limitations fast:

{ "items": [ { "id": "string", "metadata": { "tags": ["required", "array"], "conditionalField": "only if type=premium" } } ], "additionalProperties": false // Strict validation }

Issues with complex schemas:

  • Nested objects with additionalProperties: false often violated
  • Conditional fields ("only include X if Y") frequently ignored
  • Enum constraints sometimes produce invalid values

Error handling: Some models fail gracefully with validation errors. Others produce malformed JSON that crashes your parser.

Automated tool usage reliability varies dramatically between providers. Some excel at using multiple tools simultaneously but struggle with error handling. Others provide rock-solid single tool usage but can't coordinate multiple systems.

Test with your specific use cases. Don't rely on general claims.

Agentic Workflows & Autonomous Systems

Building agents that can use tools, navigate interfaces, and complete multi-step tasks autonomously? This is where model differences matter most.

Claude 4.5 Sonnet dominates agentic use cases:

Why Claude wins for agents:

The research shows Claude maintains consistent performance across different problem-solving approaches without requiring special configurations. GPT-5 has variable latency depending on reasoning depth—unpredictable for autonomous workflows.

When to use what:

  • Building coding agents: Claude 4.5 Sonnet (tool orchestration, file management, command execution)
  • Browser automation: Claude 4.5 Sonnet (best at GUI interactions)
  • Customer service agents: Claude 4.5 Haiku (fast enough for real-time, good enough for tool use)
  • Analytical agents: GPT-5 if you need peak reasoning and can tolerate variable latency

Critical consideration: Developer reports note that "Claude Sonnet 4.5 is sensitive to prompt structure" for agent mode. Set high-level contract once, keep task prompts short.

Speed & Latency: Real Performance Numbers

Performance capabilities matter. But speed determines user experience.

Time-to-First-Token (TTFT)—the delay before a model starts generating output:

When speed matters, the choice is clear-cut. Interactive applications with humans in the loop need sub-second response times. Background batch processing can tolerate higher latency for better quality or lower costs.

Real-time applications benefit from cascading: start with fast, cheap models for initial filtering. Use expensive models only when necessary.

Infrastructure optimization varies by provider. Some models benefit significantly from caching. Others show minimal improvement. Streaming responses can dramatically improve perceived performance—but implementation complexity varies across APIs.

Cost Analysis: The Full Economic Picture

Base Pricing Reality

ModelInput (per 1M tokens)Output (per 1M tokens)Context Window
GPT-5$10.00$30.00128K
Claude 4.5 Sonnet$3.00$15.00200K
GPT-4o$2.50$10.00128K
Claude 4.5 Haiku$1.00$5.00200K
Claude 3.5 Sonnet$3.00$15.00200K
GPT-4o-mini$0.15$0.60128K

Optimization features can dramatically reduce costs.

Prompt caching: Claude gives you 90% cost reduction on cached content at $0.30 per million tokens. Highly effective for applications that reuse system prompts or reference materials.

Batch processing: Non-real-time use cases achieve 50% cost reductions across all major providers. Essential for high-volume background tasks like document analysis or data processing pipelines.

Efficient context management: Smart use of extended context is often more cost-effective than making multiple API calls—especially when you need the model to synthesize information across large documents.

Real-World Cost Scenarios

Processing 10,000 support tickets monthly (500 input tokens, 200 output tokens):

  • GPT-4o-mini: ~$40/month
  • GPT-4o: ~$325/month
  • GPT-5: ~$1,100/month

Daily chatbot with 5,000 users (1,000 input tokens, 300 output tokens):

  • GPT-4o-mini: ~$675/month
  • GPT-4o: ~$4,500/month
  • GPT-5: ~$14,000/month

These assume no optimization. With prompt caching and batch processing, costs drop 50-90%.

Hidden Costs

Sticker price tells part of the story. Factor in:

  • Failed requests and retries: Some models fail more, increasing actual costs
  • Development overhead: Complex prompt engineering increases time-to-deployment
  • Monitoring infrastructure: Different models need different observability approaches
  • Quality issues: Lower-quality outputs need human review or reprocessing

Context Windows & Practical Limits

A context window determines how much text a model can process at once.

Maximum capabilities:

Real-world considerations matter more than theoretical limits:

  • Lost in the middle: Information buried in large contexts gets ignored or misprocessed
  • Cost: Large contexts increase processing costs significantly
  • Processing time: Larger contexts mean slower response times
  • Quality degradation: Models struggle to maintain quality across very large contexts

For most applications, structured approaches beat massive context windows.

Retrieval Augmented Generation (RAG)—having a research assistant find relevant information first, then using that to generate responses—proves more reliable than stuffing everything into a massive context window.

When to use what:

  • Large context: You need the model to synthesize across the entire document set
  • RAG: You need precise retrieval with cost optimization and better reliability

This architectural choice impacts both performance and cost at scale.

API Capabilities & Integration Reality

Function Calling & Structured Output

Theoretical capabilities diverge from practical reliability. Models claim support for complex automated tool usage and structured output. Real-world performance varies significantly.

JSON Schema Mode support is inconsistent. GPT-5 introduces native JSON Schema Mode with guaranteed 100% schema compliance. Claude 4.5 models demonstrate high reliability for structured output with 0% error rate on internal benchmarks.

Test thoroughly with your specific schema requirements.

Using multiple tools simultaneously adds complexity that introduces failures. Many applications achieve better reliability with sequential tool usage—even if it increases latency.

Rate Limits & Availability

Rate limiting matters for production. The tier-based systems mean spending more money literally buys you higher limits.

OpenAI Tiers (based on cumulative spending):

  • Tier 1 ($5 spent): 60 requests/minute, 500K tokens/minute
  • Tier 5 ($1,000 spent): 1,000 requests/minute, 10M tokens/minute

Anthropic limits are more conservative but offer batch processing discounts that offset restrictions for non-real-time applications.

Task-to-Model Decision Framework

How to actually choose:

High-Volume, Cost-Sensitive Tasks

Processing thousands of requests daily with tight budget constraints? GPT-4o-mini is the practical choice. The performance differences don't justify the dramatic cost increase for high-volume scenarios.

Optimization strategies:

  • Implement prompt caching for repeated content
  • Use batch processing for non-real-time workflows (50% cost reductions)
  • Cascade models: cheap models for filtering, expensive models for edge cases

Code Generation & Technical Work

Simple completion and debugging? GPT-4o-mini provides excellent value.

Complex architectural decisions or need the highest coding performance? Claude 4.5 Sonnet leads with 77.2% SWE-bench score (82% with test-time compute), beating GPT-5's 74.9%—and without the latency issues.

By task:

  • Simple completion: GPT-4o-mini
  • Complex debugging and architecture: Claude 4.5 Sonnet (77.2% SWE-bench, best overall coding model)
  • Peak reasoning with latency tolerance: GPT-5 (74.9% SWE-bench) ⚠️ Warning: Severe latency issues
  • Documentation generation: All major models perform similarly

For most teams, Claude 4.5 Sonnet provides the best balance of performance, speed, and cost for complex coding tasks.

Complex Reasoning & Analysis

Running multi-step analytical workflows where errors compound? Premium models like GPT-5 or Claude 4.5 Sonnet typically justify their cost.

But don't default to expensive models automatically. Test whether cheaper models with better prompting achieve similar results.

When premium models are essential:

  • Multi-step analytical workflows where errors compound
  • Legal or medical contexts where accuracy is paramount
  • Novel problem domains where the model needs to reason from first principles

Critical trade-off for GPT-5: Superior reasoning capability comes with variable latency depending on reasoning depth. For batch analytical jobs this is acceptable. For interactive analysis, Claude 4.5 Sonnet's predictable response times (~1.98s TTFT) may be more practical despite lower peak performance.

Real-Time/Interactive Applications

"Real-time" means different things depending on your use case. Here's what actually matters:

Conversational AI / Chatbots (Target: <1s TTFT)

Users expect natural conversation flow. 1-2 second delays are acceptable.

Recommended:

  • Claude 4.5 Haiku: Near-frontier performance, 2-4x faster than Sonnet, 73.3% SWE-bench
  • Claude 3.5 Haiku: 0.36s TTFT (fastest available)
  • GPT-4o-mini: Consistently fast, good balance of speed and capability

Search & Autocomplete (Target: <500ms TTFT)

Users notice delays above half a second. Every 100ms matters.

Recommended:

  • Claude 3.5 Haiku: 360ms TTFT meets requirements
  • GPT-4o-mini: Fast enough for most autocomplete use cases
  • Optimization required: Use prompt caching, streaming responses

Live Collaboration (Target: <2s acceptable)

Code editors, document collaboration, design tools. Users tolerate brief waits for intelligent suggestions.

Recommended:

  • Claude 4.5 Sonnet: ~1.98s TTFT acceptable for this use case
  • GPT-4o: Good balance of speed and quality
  • Claude 4.5 Haiku: If you need faster responses

Gaming / High-Frequency Interactive (Target: <200ms)

Real-time gaming, live event processing, high-frequency trading.

Reality check: Current LLMs don't meet these requirements reliably. Consider:

  • Alternative architectures: Smaller, fine-tuned models
  • Pre-computed responses: Cache common interactions
  • Hybrid approaches: Rule-based systems with occasional LLM calls

When NOT to Use AI Models

Sometimes the best model choice is no model at all. LLMs aren't the right solution when:

Deterministic logic required:

  • Mathematical calculations: Use code, not LLMs (1+1 should always equal 2)
  • Business rules enforcement: Rule engines are faster, cheaper, auditable
  • Exact string matching: Regex and traditional search beat LLMs on speed and accuracy

Latency is critical (<100ms):

  • High-frequency trading, real-time bidding, instant validation
  • Current LLMs can't reliably meet these requirements
  • Use caching, pre-computation, or traditional algorithms

Compliance and auditability:

  • Regulatory environments requiring explainable decisions
  • LLMs are black boxes—hard to explain why they made a specific choice
  • Use traditional ML with feature importance or rule-based systems

Extreme volume with tight budgets:

  • Millions of simple requests per day
  • Even $0.15 per 1M tokens adds up: 10M requests/day = $1,500/day on GPT-4o-mini
  • Consider: traditional ML classifiers cost pennies at that scale

Data privacy constraints:

  • Can't send data to external APIs (healthcare PHI, financial PII)
  • Self-hosted LLMs exist but add significant infrastructure complexity
  • Sometimes traditional on-premise solutions are more practical

When simple solutions work:

  • Template-based responses for FAQs
  • Keyword matching for basic classification
  • Don't use a sledgehammer for thumbtacks

Benchmarks vs. Reality: Developer Experience

Where Benchmarks Don't Tell the Full Story

Standard benchmarks test isolated capabilities under ideal conditions. Production faces different challenges.

Prompt sensitivity varies dramatically between models. Some require extensive prompt engineering to achieve consistent results. Others work reliably with simple, direct instructions.

Consistency versus peak performance: Some models deliver exceptional results on their best attempts but struggle with reliability. Others provide "good enough" results consistently.

What Developers Actually Report

The Stack Overflow survey:

  • 84% of developers use AI tools
  • Only 29% trust AI accuracy
  • 66% are frustrated by "almost right" solutions that require significant debugging
  • Claude 3.5 Sonnet is the most admired model (51.2% developer preference)—despite not being the most used

Community consensus:

  • Raw benchmark performance correlates poorly with development productivity
  • Model choice matters less than prompt engineering and integration quality
  • Cost optimization often provides better ROI than model upgrades

Production Engineering: Making It Work

Testing & Evaluation

Model comparison pipelines require methodical approaches:

  1. Define success metrics specific to your use case—not generic benchmarks
  2. A/B testing should account for prompt sensitivity and model consistency
  3. Cost tracking needs to include both successful and failed requests

Evaluate models across accuracy, cost per request, average latency, and error rates. Don't rely solely on published benchmarks.

Multi-Model Strategies

Implement routing logic: simple requests to cost-effective models, complex requests to premium models. This cascading approach works well for content moderation pipelines, customer service escalation, and document processing workflows.

Implementation patterns: Try cheaper models first, escalate to premium models when confidence scores fall below acceptable thresholds.

Monitoring in Production

Essential metrics go beyond simple success rates:

  • Token usage patterns and cost trends
  • Latency percentiles and timeout rates
  • Quality metrics specific to your application

Detecting degradation: Establish baselines and alert thresholds. Model performance can change with provider updates. Monitor continuously:

  • Requests per minute
  • Cost per hour
  • Latency percentiles
  • Error rates
  • Application-specific quality scores

Tracing & Evals: The Missing Piece

The model selection decisions in this guide assume you have proper observability. Without tracing and evaluation systems, you can't know if your model choice is actually working.

What proper observability looks like:

  • Request tracing: Every prompt, response, latency, and cost logged (you should inspect your traces frequently)
  • Automated & Human evals: Tests validating output quality, accuracy, and format compliance and human review of the results
  • Performance dashboards: Real-time visibility across your entire LLM fleet

This is a deep topic that deserves its own detailed guide.

Common Pitfalls & Things to Watch Out For

Selection Mistakes

Premature optimization: Many teams choose expensive models before understanding their actual requirements. Start with baseline models. Upgrade based on measured needs.

Ignoring compound latency: Multi-step workflows can make fast models seem slow when chained together.

Underestimating context needs: Often leads to model changes mid-development when extended context becomes necessary.

Integration Challenges

API reliability varies between providers. Some handle rate limiting gracefully. Others fail hard. Build appropriate retry logic and fallback strategies.

Version management: Critical as models update. Pin specific versions for production stability.

Error handling: Include exponential backoff for rate limits and fallback model calls for persistent failures.

Cost Surprises

Context window traps: Processing large documents inefficiently can dramatically increase costs.

Development versus production: Usage patterns differ significantly. Budget for testing, experimentation, and gradual rollout phases.

Keeping Current

The AI model landscape evolves rapidly.

Version management should balance stability with improvement opportunities:

  • Pin models for production stability
  • Test new releases in staging environments
  • Monitor provider communication for deprecation notices

Track updates across multiple sources:

  • Provider blogs and documentation updates
  • Developer community discussions and benchmarks
  • Third-party evaluation platforms

FAQ

1. Should I start with the most powerful/expensive model?

No. Start with cost-effective baseline models like GPT-4o-mini. Upgrade only when specific limitations impact your application. Performance convergence means expensive models often provide minimal benefit for typical tasks.

2. How do I benchmark for my specific use case?

Create evaluation datasets from your actual data. Generic benchmarks rarely predict performance on your specific problem domain. Test with representative prompts, edge cases, and failure scenarios.

3. When is a cheaper model "good enough"?

When cost savings outweigh quality improvements. When downstream processes can handle occasional errors. Many applications benefit more from processing 10x more data with a cheaper model than perfect results on limited data.

4. Should I use different models for different tasks?

Yes—when complexity and volume justify the implementation overhead. Simple routing logic can optimize cost-performance across different request types.

5. How do I handle model inconsistency?

Implement retry logic with different prompts. Use majority voting for critical decisions. Cascade to more reliable models for inconsistent results.

6. Can I mix providers in one application?

Yes, but consider the operational complexity. Different APIs, rate limits, and pricing structures increase implementation and monitoring overhead.

Making the Right Choice

The explosion of AI model options creates both opportunities and complexity.

The most successful implementations focus on practical engineering principles. Not chasing benchmark leaderboards.

Three principles:

1) Start with baselines, not optimization. Choose cost-effective models that meet your basic requirements. Optimize based on real usage patterns and measured limitations.

2) Let real data drive decisions. Production constraints often matter more than benchmarks. Rate limits, latency requirements, and integration complexity determine success more than marginal accuracy improvements.

3) Build adaptable systems. Don't bet everything on today's leader. The AI model landscape will continue evolving rapidly. Build systems that can adapt to new models and providers.

The best model choice delivers value consistently while maintaining flexibility for tomorrow's opportunities.