Which is cheaper, GPT-4o or Jamba 1.5 Large?
As of August 20, 2026, Jamba 1.5 Large is 20% cheaper on blended LLM API pricing. Jamba 1.5 Large costs $2.00 per million input tokens and $8.00 per million output tokens ($10.00 blended 1M-in + 1M-out). GPT-4o costs $2.50 / $10.00 per million tokens ($12.50 blended). Sources: https://developers.openai.com/api/docs/pricing and https://anotherwrapper.com/tools/llm-pricing. The cheapest LLM for your app still depends on how many output tokens you generate.
How much does GPT-4o vs Jamba 1.5 Large cost per million tokens?
GPT-4o OpenAI API pricing is $2.50 input and $10.00 output per million tokens via OpenAI. Jamba 1.5 Large AI21 Labs API pricing is $2.00 input and $8.00 output per million tokens via AI21 Labs. Use those four numbers, not ChatGPT Plus or Claude Pro subscription prices, when you are comparing APIs.
Which is better for coding, GPT-4o or Jamba 1.5 Large?
GPT-4o leads on HumanEval: GPT-4o at 90.2% vs Jamba 1.5 Large at 71.3%. SWE-bench measures real GitHub fixes; LiveCodeBench measures contest programming.
What is the context window for GPT-4o vs Jamba 1.5 Large?
GPT-4o supports a 128K token context window, while Jamba 1.5 Large supports 256K tokens. Jamba 1.5 Large offers a larger context window. Filling a larger window bills more input tokens, so the cheaper-per-million model can still cost more on long documents.
Which model performs better on benchmarks, GPT-4o or Jamba 1.5 Large?
GPT-4o leads on 4 of 5 shared benchmarks versus Jamba 1.5 Large's 1 wins. Check the comparison table for GPQA Diamond, SWE-bench, MMLU, HLE, and the other evals we track. Token price and benchmark score together are the usual LLM comparison, not either number alone.
Is GPT-4o or Jamba 1.5 Large better for production use?
Both GPT-4o and Jamba 1.5 Large are production API models. For cost-sensitive production traffic, Jamba 1.5 Large has better economics on the blended 1M-in + 1M-out scale. For maximum capability, weight the benchmark table for your domain. Many production stacks route cheap models for drafts and a frontier model for the hard turn, which is usually cheaper than sending everything to the expensive API.
Can I switch between GPT-4o and Jamba 1.5 Large in my app?
Yes. Call GPT-4o through OpenAI and Jamba 1.5 Large through AI21 Labs with separate API keys, or through a gateway that already wraps both. Keep prompts in tokens, not characters, when you estimate the invoice. AnotherWrapper templates can swap providers without rewriting auth and billing.
How accurate is this GPT-4o vs Jamba 1.5 Large pricing data?
List rates are the latest published OpenAI API pricing and AI21 Labs API pricing, quoted per million tokens. Official rate sheets: https://developers.openai.com/api/docs/pricing and https://anotherwrapper.com/tools/llm-pricing. Prompt caching, batch APIs, and committed-use discounts can change the invoice. Benchmark scores are from official publications and independent evals, updated as new numbers land.
What is the output token limit for GPT-4o and Jamba 1.5 Large?
GPT-4o supports up to 4K output tokens per request, while Jamba 1.5 Large supports up to 256K output tokens. Output tokens are usually the expensive half of API pricing, so a higher max output cap is a capability, not a discount.
Which model has better throughput, GPT-4o or Jamba 1.5 Large?
GPT-4o runs at approximately 100 tokens/second while Jamba 1.5 Large runs at 42 tokens/second. GPT-4o is faster in raw throughput. Throughput (tokens per second) and time-to-first-token are why two models with the same per-million-token price can still feel different in chat and agents.
How do I switch the models in this GPT-4o vs Jamba 1.5 Large comparison?
Use the model pickers at the top of this LLM comparison. Search any model in the index and the URL updates. Open GPT-4o or Jamba 1.5 Large from the sidebar for the single-model API pricing page, or go back to the LLM pricing table to sort by cheapest blended cost.
