GPT-3.5 Turbo vs Jamba 1.5 Large
Jamba 1.5 Large leads the LLM Stats Score 1.2 to -9.2. GPT-3.5 Turbo is 4.7x cheaper per token.
OpenAI · AI21 Labs · Updated for 2026
Which is better?
Jamba 1.5 Large leads the overall LLM Stats Score 1.2 to -9.2, ranking #309 overall.
In the 2 individual benchmarks reported for both models, Jamba 1.5 Large wins 2; this is a narrower head-to-head signal than the composite indexes.
On price, GPT-3.5 Turbo is roughly 4.7x cheaper per token on a blended 3:1 input/output basis, which adds up quickly at production volume.
Jamba 1.5 Large also accepts a larger context window (256,000 input tokens), making it the stronger choice for long documents and large codebases.
Based on current LLM Stats indexes, shared benchmarks, pricing, and model metadata for 2026.
Choose GPT-3.5 Turbo
- cost matters — it's about 4.7x cheaper per token
Choose Jamba 1.5 Large
- overall performance matters — it scores 1.2 and ranks #309 on LLM Stats
- your work emphasizes reasoning — it leads those capability indexes
- you value its reported benchmark strengths — it wins 2 of 2 exact shared results
- you process long inputs — it offers a 256,000 token context window
- you want the most recent training data — it shipped Aug 2024
- you need open weights you can self-host or fine-tune
At a glance
The differences that matter most.
Capability indexes
Additional strengths measured across groups of related public benchmarks
Individual benchmarks
8 reported for GPT-3.5 Turbo · 8 for Jamba 1.5 Large
GPT-3.5 Turbo outperforms in 0 benchmarks, while Jamba 1.5 Large is better at 2 benchmarks (GPQA, MMLU).
Jamba 1.5 Large significantly outperforms across most benchmarks.
Human preference
Blind head-to-head votes and playground preference scores
Pricing Analysis
Price comparison per million tokens
For input processing, GPT-3.5 Turbo ($0.50/1M tokens) is 4.0x cheaper than Jamba 1.5 Large ($2.00/1M tokens).
For output processing, GPT-3.5 Turbo ($1.50/1M tokens) is 5.3x cheaper than Jamba 1.5 Large ($8.00/1M tokens).
In conclusion, Jamba 1.5 Large is more expensive than GPT-3.5 Turbo.*
* Using a 3:1 ratio of input to output tokens
Context Window
Maximum input and output token capacity
Jamba 1.5 Large accepts 256,000 input tokens compared to GPT-3.5 Turbo's 16,385 tokens. Jamba 1.5 Large can generate longer responses up to 256,000 tokens, while GPT-3.5 Turbo is limited to 4,096 tokens.
License
Usage and distribution terms
GPT-3.5 Turbo is licensed under a proprietary license, while Jamba 1.5 Large uses Jamba Open Model License.
License differences may affect how you can use these models in commercial or open-source projects.
Proprietary
Closed source
Jamba Open Model License
Open weights
Release Timeline
When each model was launched
GPT-3.5 Turbo was released on 2023-03-21, while Jamba 1.5 Large was released on 2024-08-22.
Jamba 1.5 Large is 17 months newer than GPT-3.5 Turbo.
Mar 21, 2023
3.5 years ago
Aug 22, 2024
2.0 years ago
1.4yr newerKnowledge Cutoff
When training data ends
GPT-3.5 Turbo has a knowledge cutoff of 2021-09-30, while Jamba 1.5 Large has a cutoff of 2024-03-05.
Jamba 1.5 Large has more recent training data (up to 2024-03-05), making it potentially better informed about events through that date compared to GPT-3.5 Turbo (2021-09-30).
Sep 2021
Mar 2024
2.5 yr newerProvider Availability
GPT-3.5 Turbo is available from Azure, OpenAI. Jamba 1.5 Large is available from Bedrock, Google.
GPT-3.5 Turbo
Jamba 1.5 Large
Outputs Comparison
Judge for yourself.
Run your own prompts against GPT-3.5 Turbo and Jamba 1.5 Large side-by-side, then vote on the output you prefer.
FAQ
Common questions about GPT-3.5 Turbo vs Jamba 1.5 Large.