Inference Labs › Compare models › Claude Sonnet 4.5 vs Gemini 2.5 Pro
Claude Sonnet 4.5 vs Gemini 2.5 Pro
Side-by-side comparison of Claude Sonnet 4.5 and Gemini 2.5 Pro: per-token pricing, context windows, strengths, and which to pick for production workloads.
Claude Sonnet 4.5
Strengths
- Best-in-class on long-context recall and instruction-following.
- Detailed tool-use traces for agentic workflows.
- Strong safety profile out of the box.
Trade-offs
- Output token price is the highest in the balanced tier.
- Throughput caps tighter than OpenAI peers on most accounts.
Gemini 2.5 Pro
Strengths
- 2M token context.
- Strong multimodal (image + video understanding).
- Native Google Search grounding for factuality.
Trade-offs
- Tool-use traces less detailed than Claude.
- Provider lock-in pulls workloads onto Google Cloud.
Side-by-side pricing
| Metric | Claude Sonnet 4.5 | Gemini 2.5 Pro |
|---|---|---|
| Input price ($/1M tokens) | $3.00 | $1.25 |
| Output price ($/1M tokens) | $15.00 | $10.00 |
| Context window | 200K tokens | 2,000K tokens |
| Cost for 1M in + 1M out | $18.00 | $11.25 |
| Tier | premium | premium |
Which should you pick?
Gemini 2.5 Pro is cheaper per 1M input tokens; Gemini 2.5 Pro is cheaper on output. For high-output workloads (long generation, structured extraction), the output rate dominates.
For long-context use cases, Gemini 2.5 Pro has the larger window (2,000K vs 200K).
In practice, the right answer depends on your traffic mix. Inference Labs lets you route the same request to whichever of these two is best for that prompt -- using policy you define (quality-first, cost-first, latency-first) without changing your application code.
Route between them automatically
Inference Labs is a vendor-neutral router that sits in front of Claude Sonnet 4.5, Gemini 2.5 Pro, and the other major cloud LLMs. Set per-request policy (max cost, max latency, required quality tier) and the router picks the best model per call. You keep your existing provider accounts -- we just orchestrate.
- Allowlist only the models your team has approved.
- Automatic failover if one provider is degraded.
- Semantic + exact-hash cache reduces redundant calls.
- Per-workspace spend caps and audit-ready trace logs.
Stop hard-coding model names. Start a free Inference Labs trial → -- 500 credits, no card required, see your routing decisions in the dashboard within minutes.