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Jamba 1.5 Large vs Phi-3.5-MoE-instruct

Jamba 1.5 Large and Phi-3.5-MoE-instruct are closely matched at 1.1 and 1.9 on the LLM Stats Score.

AI21 Labs · Microsoft · Updated for 2026

Which is better?

Jamba 1.5 Large and Phi-3.5-MoE-instruct are closely matched on the overall LLM Stats Score at 1.1 and 1.9.

In the 7 individual benchmarks reported for both models, Jamba 1.5 Large wins 5; this is a narrower head-to-head signal than the composite indexes.

Based on current LLM Stats indexes, shared benchmarks, pricing, and model metadata for 2026.

Choose Jamba 1.5 Large

  • you value its reported benchmark strengths — it wins 5 of 7 exact shared results

Choose Phi-3.5-MoE-instruct

  • you want the most recent training data — it shipped Aug 2024

At a glance

The differences that matter most.

Core performance indexes
1.1
#310
1.9
#306
1.1
#300
1.4
#299
Cost, coverage & limits
Benchmark wins
5 of 7
2 of 7
Input price
$2.00 / M
— / M
Output price
$8.00 / M
— / M
Context window
256,000

Capability indexes

Additional strengths measured across groups of related public benchmarks

3 shared
Index
Jamba 1.5 Large
Phi-3.5-MoE-instruct
4.9#272
4.7#274
5.8#159
4.8#167
5.8#147
4.8#154
Conservative TrueSkill rating · higher is betterHow scores work

Individual benchmarks

8 reported for Jamba 1.5 Large · 31 for Phi-3.5-MoE-instruct

7 shared

Jamba 1.5 Large outperforms in 5 benchmarks (ARC-C, Arena Hard, GPQA, MMLU, MMLU-Pro), while Phi-3.5-MoE-instruct is better at 2 benchmarks (GSM8k, TruthfulQA).

Jamba 1.5 Large shows notably better performance in the majority of benchmarks.

Mon Sep 07 2026 • llm-stats.com

Human preference

Blind head-to-head votes and playground preference scores

Model Size

Parameter count comparison

338.0B diff

Jamba 1.5 Large has 338.0B more parameters than Phi-3.5-MoE-instruct, making it 563.3% larger.

AI21 Labs
Jamba 1.5 Large
398.0Bparameters
Microsoft
Phi-3.5-MoE-instruct
60.0Bparameters
398.0B
Jamba 1.5 Large
60.0B
Phi-3.5-MoE-instruct

Context Window

Maximum input and output token capacity

Only Jamba 1.5 Large specifies input context (256,000 tokens). Only Jamba 1.5 Large specifies output context (256,000 tokens).

AI21 Labs
Jamba 1.5 Large
Input256,000 tokens
Output256,000 tokens
Microsoft
Phi-3.5-MoE-instruct
Input- tokens
Output- tokens
Mon Sep 07 2026 • llm-stats.com

License

Usage and distribution terms

Jamba 1.5 Large is licensed under Jamba Open Model License, while Phi-3.5-MoE-instruct uses MIT.

License differences may affect how you can use these models in commercial or open-source projects.

Jamba 1.5 Large

Jamba Open Model License

Open weights

Phi-3.5-MoE-instruct

MIT

Open weights

Release Timeline

When each model was launched

Jamba 1.5 Large was released on 2024-08-22, while Phi-3.5-MoE-instruct was released on 2024-08-23.

Phi-3.5-MoE-instruct is 0 month newer than Jamba 1.5 Large.

Jamba 1.5 Large

Aug 22, 2024

2.0 years ago

Phi-3.5-MoE-instruct

Aug 23, 2024

2.0 years ago

1d newer

Knowledge Cutoff

When training data ends

Jamba 1.5 Large has a documented knowledge cutoff of 2024-03-05, while Phi-3.5-MoE-instruct's cutoff date is not specified.

We can confirm Jamba 1.5 Large's training data extends to 2024-03-05, but cannot make a direct comparison without Phi-3.5-MoE-instruct's cutoff date.

Jamba 1.5 Large

Mar 2024

Phi-3.5-MoE-instruct

Outputs Comparison

Notice missing or incorrect data?Start an Issue discussion

Judge for yourself.

Run your own prompts against Jamba 1.5 Large and Phi-3.5-MoE-instruct side-by-side, then vote on the output you prefer.

Jamba 1.5 Large
✓ Preferred
Phi-3.5-MoE-instruct
Open in Playground

FAQ

Common questions about Jamba 1.5 Large vs Phi-3.5-MoE-instruct.

Which is better, Jamba 1.5 Large or Phi-3.5-MoE-instruct?

Jamba 1.5 Large and Phi-3.5-MoE-instruct are closely matched on the LLM Stats Score at 1.1 and 1.9. Jamba 1.5 Large is made by AI21 Labs and Phi-3.5-MoE-instruct is made by Microsoft. The best choice depends on your use case — compare their capability indexes, individual benchmarks, pricing, and limits above.

How does Jamba 1.5 Large compare to Phi-3.5-MoE-instruct in benchmarks?

Jamba 1.5 Large scores ARC-C: 93.0%, GSM8k: 87.0%, MMLU: 81.2%, Arena Hard: 65.4%, TruthfulQA: 58.3%. Phi-3.5-MoE-instruct scores ARC-C: 91.0%, OpenBookQA: 89.6%, GSM8k: 88.7%, PIQA: 88.6%, RULER: 87.1%.

What are the context window sizes for Jamba 1.5 Large and Phi-3.5-MoE-instruct?

Jamba 1.5 Large supports 256K tokens and Phi-3.5-MoE-instruct supports an unknown number of tokens. A larger context window lets you process longer documents, conversations, or codebases in a single request.

What are the main differences between Jamba 1.5 Large and Phi-3.5-MoE-instruct?

Key differences include LLM Stats Score (1.1 vs 1.9), licensing (Jamba Open Model License vs MIT). See the full comparison above for benchmark-by-benchmark results.

Who makes Jamba 1.5 Large and Phi-3.5-MoE-instruct?

Jamba 1.5 Large is developed by AI21 Labs and Phi-3.5-MoE-instruct is developed by Microsoft.