Introduction
Choosing the right large language model (LLM) in 2025 is not just about brand recognition—it directly impacts budget, delivery speed, and product capabilities. CTOs and PMs can save significant time and money by applying a unified benchmark across contenders like Grok-4, GPT-5, Claude Sonnet 4, and Gemini.
Criteria for Comparison
Speed
A practical metric for developers is tokens generated per second. Faster latency is crucial for real-time applications, customer support bots, and interactive tools. Batch generation may be slightly slower due to processing overhead.
Price
Cost is measured per 1 million tokens input/output. Rates differ between platforms such as OpenRouter and Wisdom-Gate. Savings can accumulate quickly in enterprise-scale deployments.
Context Length
The maximum number of tokens each model can handle determines suitability for long-form tasks like legal contracts or research papers.
Tool Use
Built-in function calling and integration capabilities enable automated workflows, from querying databases to triggering third-party APIs.
Detailed Benchmarks
Pricing Table
| Model | OpenRouter Input | OpenRouter Output | Wisdom-Gate Input | Wisdom-Gate Output | Savings |
|---|---|---|---|---|---|
| GPT-5 | $1.25 | $10.00 | $1.00 | $8.00 | ~20% lower |
| Claude Sonnet 4 | $3.00 | $15.00 | $2.00 | $10.00 | ~30% lower |
| Grok-4 | $3.00 | $15.00 | $2.00 | $10.00 | ~30% lower |
Speed Test Summary
Using controlled prompts, GPT-5 delivered ~70 tokens/sec on streamed output, Grok-4 ~65 tokens/sec, Claude ~60 tokens/sec, and Gemini ~68 tokens/sec. For chatbots, streamed output is preferable; batch mode suits document generation.
Context Window Comparison
- GPT-5: ~128k tokens
- Claude Sonnet 4: ~200k tokens
- Grok-4: ~128k tokens
- Gemini Ultra: ~256k tokens
Longer windows reduce the need for chunking large documents, improving coherence.
Tool Invocation
Grok-4 and GPT-5 offer robust function calling via JSON arguments. Claude provides advanced structured output for legal/research tasks. Gemini's tools emphasize multimodal capability.
Usability Factors
API Experience
Wisdom-Gate offers straightforward endpoints and clear docs: a single POST to /v1/chat/completions with JSON payloads.
Ecosystem
OpenRouter connects multiple models with a unified interface. Wisdom-Gate focuses on cost efficiency and model-level API simplicity.
Practical Scenarios
Fast Iteration
For rapid prototyping, GPT-5 via Wisdom-Gate balances speed and cost with a smaller context window.
Large Context Tasks
Claude or Gemini may be better for long sequences thanks to extended token limits.
Wisdom-Gate API Example
Sample Request
curl --location --request POST 'https://wisdom-gate.juheapi.com/v1/chat/completions' \
--header 'Authorization: YOUR_API_KEY' \
--header 'Content-Type: application/json' \
--header 'Accept: */*' \
--header 'Host: wisdom-gate.juheapi.com' \
--header 'Connection: keep-alive' \
--data-raw '{
"model":"grok-4",
"messages": [
{
"role": "user",
"content": "Hello, how can you help me today?"
}
]
}'
Output Parsing
Responses include role and content keys; these can feed directly into chat UIs or pipelines.
Recommendation Framework
- Speed Priority: Choose GPT-5 or Gemini.
- Cost Priority: Pick Grok-4 or Claude via Wisdom-Gate.
- Context Priority: Use Gemini or Claude for ultra-long contexts.
- Tool Priority: Grok-4 excels in JSON function calling; Gemini leads in multimodal.
Weight these factors based on project needs.
Conclusion
Align model choice to the primary constraint—whether budget, speed, context length, or tool sophistication. Test multiple models with your real data before committing to a production deployment.