gpt-5.6-sol
Top capability tier for complex professional work, reasoning, and coding
CapabilityflagshipSpeedquality-firstEfficiencypremium-priced
MODEL COMPARISON
Choose two models. Generate a decision-ready comparison.
Choose two specific models from Model Plaza and compare integration, capabilities, cost, workload fit, and launch risk.
01
gpt-5.6-sol VS claude-sonnet-5
Choose gpt-5.6-sol: Choose when capability ceiling matters more than price and the task can use high reasoning effort. Choose claude-sonnet-5: Start most Claude coding and AI agent workloads with Sonnet. On rates, claude-sonnet-5 has lower input cost; claude-sonnet-5 has lower output cost.
claude-sonnet-5
Claude's main model for coding and AI agents
CapabilityhighSpeedfastEfficiencyexcellent balance
Full specification and capability check
Capability labels are qualitative; verify performance on real tasks
| Comparison area | gpt-5.6-sol | claude-sonnet-5 |
|---|---|---|
| Model ID | gpt-5.6-sol | claude-sonnet-5 |
| Provider / API key access | OpenAI · ChatGPT(Codex) | Anthropic · Claude Code |
| LLMFly billing multiplier | 0.3x | 0.5x |
| LLMFly input / 1M | $1.5 | $1 |
| LLMFly output / 1M | $9 | $5 |
| Cache write / 1M | $1.875 | $1.25 |
| Cache read / 1M | $0.15 | $0.1 |
| Vendor reference input / output | $5 / $30 | $2 / $10 |
| Pricing tiers | ≤272K / >272K | Single rate |
| Vendor lifecycle | Current flagship | Currently supported by Anthropic |
| Product position | Top capability tier for complex professional work, reasoning, and coding | Claude's main model for coding and AI agents |
| Context / max output | 1.05M / 128K | Up to 1M, depending on platform |
| Input and output modalities | Text and image input; text output | Text and image input; text output |
| Tools and agent capabilities | Functions, web search, file search; computer use depends on model | Tool use and long-running state tracking with Claude configuration |
| Capability tier | flagship | high |
| Speed profile | quality-first | fast |
| Cost efficiency | premium-priced | excellent balance |
| Coding | complex repository work | strong coding and review |
| Reasoning | six effort levels, none–max | strong |
| Agents / tool loops | long-running multi-tool work | long-running state tracking |
| Best fit | Complex coding, deep research, long-context analysis, and high-value agents | Coding, frontend work, code review, and production AI agents |
| Poor fit | Simple classification, bulk rewriting, and highly cost-sensitive traffic | Simple high-volume requests that do not justify premium cost |
| Why choose it | Choose when capability ceiling matters more than price and the task can use high reasoning effort. | Start most Claude coding and AI agent workloads with Sonnet. |
02
Estimate one representative task
Enter representative token usage to compare base model cost per task. Retries, tool loops, and caching are additional.
gpt-5.6-sol$0.2400claude-sonnet-5$0.1500
03
Finish with a real evaluation
- 01Prepare 20–50 real tasks covering normal, edge, and failure cases.
- 02Define pass criteria before viewing outputs; keep instructions, tools, and limits equivalent.
- 03Record time to first token, total latency, tokens, retries, tool errors, and manual correction.
- 04Calculate cost per accepted result—not per call or token—and choose against workload requirements.
- 05Record the test date, model ID, API key access, and configuration; rerun after material model or price changes.