Every month there's a new benchmark declaring one model the winner. And every month the benchmark tells you almost nothing useful about which model to actually run in your agent.
Benchmarks test models in isolation. Agents run in context. The questions that matter for production agents are:
- Which model follows tool call schemas reliably?
- Which one handles long conversation context without drifting?
- Which one is most cost-effective for simple vs complex tasks?
- Which one has the fastest latency for interactive use cases?
- Which one is most consistent (not just highest average, but most reliable)?
We ran all three — Claude Sonnet 4.6, GPT-4.1, and Gemini 2.5 Flash/Pro — through real agent workloads. Here's what we found.
The Models We're Comparing
Anthropic Claude:
- Claude Haiku 4.5 — fastest, cheapest, best for classification and simple tasks
- Claude Sonnet 4.6 — balanced quality and speed, best general-purpose model
- Claude Opus 4.6 — highest capability, most expensive, best for complex reasoning
OpenAI:
- GPT-4.1-mini — fast and cheap, best for structured outputs
- GPT-4.1 — strong all-rounder, excellent for code
- o3 / o4-mini — extended thinking for math and complex reasoning
Google:
- Gemini 2.5 Flash — fastest and cheapest, excellent for high-volume tasks
- Gemini 2.5 Pro — strong reasoning, long context up to 2M tokens
Tool Calling: Which Model Actually Follows Schema?
For AI agents, tool calling reliability is more important than raw intelligence. An agent that calls tools incorrectly, ignores required parameters, or generates invalid JSON is useless in production regardless of its benchmark scores.
Test: We gave each model 100 tool calls with schemas of varying complexity, including required nested objects, optional enum fields, and arrays of objects.
Results:
| Model | Valid JSON | Schema compliant | Required params present | |-------|-----------|-----------------|------------------------| | Claude Sonnet 4.6 | 99% | 97% | 99% | | GPT-4.1 | 99% | 96% | 98% | | Gemini 2.5 Flash | 98% | 93% | 96% | | GPT-4.1-mini | 97% | 91% | 94% | | Gemini 2.5 Pro | 99% | 95% | 98% | | Claude Haiku 4.5 | 97% | 93% | 97% |
All top models perform similarly. Where they diverge: error recovery. When a tool call fails and the agent needs to retry with corrected parameters, Claude is consistently better at understanding the error and fixing the specific field, rather than regenerating the entire call from scratch.
Verdict for tool use: Claude Sonnet or GPT-4.1. Both are reliable. Claude has a slight edge on error recovery.
Long Context Performance
Agents accumulate context. After 10 turns of conversation, you're passing 5K+ tokens. After 50 turns with tool results, you're at 50K+. Which models maintain accuracy and consistency as context grows?
Test: Agent task with 100K token context (conversation history + retrieved documents). We measured whether the agent correctly referenced earlier information and maintained consistent instructions throughout.
| Model | Context Window | Accuracy at 50K tokens | Accuracy at 100K tokens | |-------|---------------|----------------------|------------------------| | Gemini 2.5 Pro | 2M tokens | 94% | 91% | | Gemini 2.5 Flash | 1M tokens | 91% | 87% | | Claude Sonnet 4.6 | 200K tokens | 96% | 93% | | Claude Opus 4.6 | 200K tokens | 97% | 95% | | GPT-4.1 | 128K tokens | 89% | 83% |
Two findings stand out:
-
Claude maintains accuracy best within its context window. Even at the upper limit, it's more consistent than GPT-4.1.
-
Gemini wins on window size. 2M tokens is not a number on paper — it's the ability to load an entire codebase, full conversation history, and all retrieved documents into a single prompt. For agents that need to reason over large corpora, Gemini 2.5 Pro is in a different class.
Verdict for long context: Gemini 2.5 Pro for very long contexts (>128K). Claude Sonnet for contexts under 200K where quality consistency matters.
Code Generation
Test: 50 coding tasks ranging from "write a Python function that..." to "refactor this 300-line TypeScript file to..." with automated test validation.
| Model | Tests passed | Compiles first try | Follows style guide | |-------|-------------|-------------------|-------------------| | Claude Sonnet 4.6 | 91% | 94% | 97% | | Claude Opus 4.6 | 94% | 96% | 98% | | GPT-4.1 | 89% | 92% | 88% | | o3 | 93% | 95% | 85% | | Gemini 2.5 Pro | 87% | 89% | 84% | | GPT-4.1-mini | 82% | 85% | 79% |
Claude leads on code, specifically because of style and convention following. GPT-4.1 generates more "functional" code but often ignores instructions about formatting, naming conventions, and patterns. Claude reads and follows the instructions more literally.
o3 is interesting — it's nearly as capable as Claude Opus on correctness but significantly less consistent about following style instructions.
Verdict for code: Claude Sonnet (balanced) or Claude Opus (maximum quality). GPT-4.1 for faster iteration when style doesn't matter.
Speed and Latency
For interactive agents (user asks, agent responds immediately), latency matters significantly. Users notice the difference between a 2-second response and an 8-second response.
Test: Time to first token on identical 500-token prompts with 500-token outputs, measured across 100 requests:
| Model | Median TTFT | p95 TTFT | Tokens/sec | |-------|------------|---------|-----------| | Gemini 2.5 Flash | 0.4s | 0.9s | 180 | | Claude Haiku 4.5 | 0.5s | 1.1s | 160 | | GPT-4.1-mini | 0.6s | 1.4s | 140 | | GPT-4.1 | 1.1s | 2.3s | 90 | | Claude Sonnet 4.6 | 1.4s | 2.8s | 80 | | Gemini 2.5 Pro | 1.8s | 3.4s | 70 | | Claude Opus 4.6 | 3.2s | 6.1s | 45 |
Gemini Flash is the fastest model available. For real-time interactive use cases, the 0.4s median TTFT is nearly instant. Claude Haiku is close and more consistent.
Verdict for latency: Gemini 2.5 Flash for maximum speed. Claude Haiku 4.5 for fast + reliable.
Cost Per Task
Raw token prices are one thing; cost per completed task is another. Models that require fewer back-and-forth turns to complete a task may be cheaper even if their per-token price is higher.
Test: 20 multi-step research tasks tracked to completion. We measured total tokens consumed across all turns to complete each task.
| Model | Price per 1M tokens (in/out) | Avg tokens per task | Cost per task | |-------|---------------------------|--------------------|--------------------| | Gemini 2.5 Flash | $0.15 / $0.60 | 4,200 | $0.0009 | | Claude Haiku 4.5 | $1.00 / $5.00 | 3,800 | $0.006 | | GPT-4.1-mini | $0.40 / $1.60 | 4,500 | $0.004 | | GPT-4.1 | $2.60 / $10.40 | 3,200 | $0.012 | | Claude Sonnet 4.6 | $3.90 / $19.50 | 2,900 | $0.017 | | Gemini 2.5 Pro | $3.50 / $10.50 | 3,400 | $0.014 | | Claude Opus 4.6 | $15.00 / $75.00 | 2,700 | $0.069 |
Gemini Flash is dramatically cheaper for high-volume tasks — 10-15x cheaper than Claude Sonnet for similar tasks. The question is whether the quality is acceptable for your use case. For classification, summarization, and simple question answering: yes, usually. For complex reasoning or code: the gap in quality may not justify the savings.
Verdict for cost: Gemini 2.5 Flash for bulk tasks. Claude Haiku for a balance. Claude Sonnet where quality matters.
How to Route Between Models Automatically
The optimal strategy isn't to pick one model — it's to match the right model to each task type. HexaClaw's smart router does this automatically based on request classification:
from openai import OpenAI
client = OpenAI(
api_key=os.getenv("HEXACLAW_API_KEY"),
base_url="https://api.hexaclaw.com/v1"
)
# Use 'auto' routing — HexaClaw picks the model
response = client.chat.completions.create(
model="auto", # Smart routing enabled
messages=[{"role": "user", "content": task}]
)
# Or specify routing preferences
response = client.chat.completions.create(
model="auto:cheap", # Route to cheapest capable model
# model="auto:fast", # Route to lowest latency model
# model="auto:quality", # Route to highest quality model
messages=[{"role": "user", "content": task}]
)
You can also route explicitly per task type in your application code:
def run_agent_task(task: str, task_type: str) -> str:
model_map = {
"classify": "gemini-2.5-flash", # Fast + cheap
"summarize": "claude-haiku-4-5", # Good + cheap
"reason": "claude-sonnet-4-6", # Quality + balanced
"code": "claude-sonnet-4-6", # Best for code
"math": "o4-mini", # Best for math
"long-context": "gemini-2.5-pro", # 2M token window
}
model = model_map.get(task_type, "claude-sonnet-4-6")
client = OpenAI(
api_key=os.getenv("HEXACLAW_API_KEY"),
base_url="https://api.hexaclaw.com/v1"
)
return client.chat.completions.create(
model=model,
messages=[{"role": "user", "content": task}]
).choices[0].message.content
With HexaClaw, all these model calls hit the same API key, deduct from the same credit balance, and appear in the same usage dashboard. No separate accounts for each provider.
The Practical Recommendation
Here's the decision tree we'd use:
Building an interactive chatbot or assistant? → Start with Gemini 2.5 Flash (speed + cost), upgrade to Claude Sonnet if quality is lacking.
Building a coding agent? → Claude Sonnet 4.6 as primary. Claude Opus for hardest tasks. GPT-4.1 as fallback.
Processing high volumes of text (classification, extraction, summarization)? → Gemini 2.5 Flash — the cost advantage at scale is significant.
Needs to reason over very long documents or codebases? → Gemini 2.5 Pro — the 2M token window is a genuine superpower here.
Complex math, logic, or scientific reasoning? → o3 or o4-mini — OpenAI's reasoning models are best for structured problem solving.
General-purpose agent where quality is paramount? → Claude Sonnet 4.6 — the most balanced model for production agent workloads.
All of these models are available through HexaClaw with one API key. Sign up at hexaclaw.com/signup and the free trial gives you credits to test each one.