Which is cheaper, Jamba 1.5 Large or Phi 4?
As of August 25, 2026, Phi 4 is 98% cheaper on blended LLM API pricing. Phi 4 costs $0.07 per million input tokens and $0.14 per million output tokens ($0.21 blended 1M-in + 1M-out). Jamba 1.5 Large costs $2.00 / $8.00 per million tokens ($10.00 blended). Sources: https://anotherwrapper.com/tools/llm-pricing and https://azure.microsoft.com/en-us/pricing/details/cognitive-services/openai-service/. The cheapest LLM for your app still depends on how many output tokens you generate.
How much does Jamba 1.5 Large vs Phi 4 cost per million tokens?
Jamba 1.5 Large AI21 Labs API pricing is $2.00 input and $8.00 output per million tokens via AI21 Labs. Phi 4 Microsoft API pricing is $0.07 input and $0.14 output per million tokens via Microsoft. Use those four numbers, not ChatGPT Plus or Claude Pro subscription prices, when you are comparing APIs.
Which is better for coding, Jamba 1.5 Large or Phi 4?
Phi 4 leads on HumanEval: Jamba 1.5 Large at 71.3% vs Phi 4 at 82.6%. SWE-bench measures real GitHub fixes; LiveCodeBench measures contest programming.
What is the context window for Jamba 1.5 Large vs Phi 4?
Jamba 1.5 Large supports a 256K token context window, while Phi 4 supports 16K 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, Jamba 1.5 Large or Phi 4?
Phi 4 leads on 5 of 6 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 Jamba 1.5 Large or Phi 4 better for production use?
Both Jamba 1.5 Large and Phi 4 are production API models. For cost-sensitive production traffic, Phi 4 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 Jamba 1.5 Large and Phi 4 in my app?
Yes. Call Jamba 1.5 Large through AI21 Labs and Phi 4 through Microsoft 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 Jamba 1.5 Large vs Phi 4 pricing data?
List rates are the latest published AI21 Labs API pricing and Microsoft API pricing, quoted per million tokens. Official rate sheets: https://anotherwrapper.com/tools/llm-pricing and https://azure.microsoft.com/en-us/pricing/details/cognitive-services/openai-service/. 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 Jamba 1.5 Large and Phi 4?
Jamba 1.5 Large supports up to 256K output tokens per request, while Phi 4 supports up to 16K 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, Jamba 1.5 Large or Phi 4?
Jamba 1.5 Large runs at approximately 42 tokens/second while Phi 4 runs at 33 tokens/second. Jamba 1.5 Large 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 Jamba 1.5 Large vs Phi 4 comparison?
Use the model pickers at the top of this LLM comparison. Search any model in the index and the URL updates. Open Jamba 1.5 Large or Phi 4 from the sidebar for the single-model API pricing page, or go back to the LLM pricing table to sort by cheapest blended cost.
