ChatGPT o3 vs Claude Thinking vs Gemini 2.5 Pro: Reasoning AI 2026

Advanced reasoning models use additional compute at inference time to think through problems before responding. Here's how they compare.

Benchmark Comparison

BenchmarkClaude Thinkingo3Gemini 2.5 Pro

MATH-50097.1%96.7%95.2% GPQA84.8%87.7%84.0% SWE-bench72.5%71.7%63.8% HumanEval94.1%92.4%90.8%

Claude Extended Thinking

python
import anthropic
client = anthropic.Anthropic()
response = client.messages.create(
    model="claude-sonnet-4-5",
    max_tokens=16000,
    thinking={
        "type": "enabled",
        "budget_tokens": 10000
    },
    messages=[{
        "role": "user",
        "content": """A startup has: Revenue $2.3M (23% YoY growth),
        Gross margin 67%, Burn $180K/mo, Cash $2.1M, ARR $1.8M, Churn 3.2%/mo.
        Should they raise Series A now or in 6 months?"""
    }]
)
for block in response.content:
    if block.type == "thinking":
        print("=== Reasoning ===")
        print(block.thinking[:500], "...")
    elif block.type == "text":
        print("=== Answer ===")
        print(block.text)

OpenAI o3

python
from openai import OpenAI
client = OpenAI()
response = client.chat.completions.create(
    model="o3",
    messages=[{"role": "user", "content": "Prove sqrt(2) is irrational using proof by contradiction."}],
    reasoning_effort="high"  # low, medium, high
)
print(response.choices[0].message.content)
print(f"Reasoning tokens: {response.usage.completion_tokens_details.reasoning_tokens}")

o3 pricing consideration:

Input: $15/1M tokens

Output: $60/1M tokens

Reasoning tokens counted as output (expensive for complex problems)

Gemini 2.5 Pro Thinking

python
import google.generativeai as genai
genai.configure(api_key="your-api-key")
model = genai.GenerativeModel(
    "gemini-2.5-pro",
    generation_config=genai.GenerationConfig(
        thinking_config=genai.ThinkingConfig(thinking_budget=8192)
    )
)
response = model.generate_content("""
    Design database schema for multi-tenant SaaS with RBAC and audit logging.
    Show SQL DDL and architectural decisions.
""")

Gemini 2.5 Pro strengths:

1M token context (entire codebases)

Best multimodal reasoning

Deep Research integration

Cost Optimization: When to Use Reasoning

python
def route_to_model(task_type: str, complexity: int) -> str:
    """
    complexity 1-10:
    1-3: simple (extract info, translate)
    4-6: moderate (analysis, multi-step)
    7-10: hard (proofs, complex decisions)
    """
    if complexity <= 3:
        return "gpt-5-mini"  # $0.40/1M
    elif complexity <= 6:
        return "claude-sonnet-4-5"  # $3/1M
    elif complexity <= 8:
        return "claude-sonnet-4-5 with thinking"
    else:
        return "o3"  # $15/1M — reserve for hardest

Task-Specific Recommendations

Mathematical Proof → o3 (best formal reasoning)

Business Decisions → Claude Extended Thinking (nuanced judgment)

Large Codebase Analysis → Gemini 2.5 Pro (1M context + reasoning)

Security Audit → Claude Thinking (finds subtle vulnerabilities)

Scientific Literature → Gemini 2.5 Pro (Deep Research + thinking)

Conclusion

Claude Extended Thinking excels at business judgment and nuanced decision-making. o3 dominates pure mathematical reasoning. Gemini 2.5 Pro wins on multimodal tasks and massive context. Default to Claude or Gemini and reserve o3 for genuinely hard problems where cost is justified.