Inkling 2 Small vs Jamba 1.5 Large
Inkling 2 Small leads the LLM Stats Score 47.2 to 0.4. Inkling 2 Small is 7.4x cheaper per token.
Thinking Machines Lab · AI21 Labs · Updated for 2026
Which is better?
Inkling 2 Small leads the overall LLM Stats Score 47.2 to 0.4, ranking #39 overall.
In the 1 individual benchmarks reported for both models, Inkling 2 Small wins 1; this is a narrower head-to-head signal than the composite indexes.
On price, Inkling 2 Small is roughly 7.4x cheaper per token on a blended 3:1 input/output basis, which adds up quickly at production volume.
Based on current LLM Stats indexes, shared benchmarks, pricing, and model metadata for 2026.
Choose Inkling 2 Small
- overall performance matters — it scores 47.2 and ranks #39 on LLM Stats
- your work emphasizes reasoning — it leads those capability indexes
- you value its reported benchmark strengths — it wins 1 of 1 exact shared results
- cost matters — it's about 7.4x cheaper per token
- you want the most recent training data — it shipped Nov 2026
Choose Jamba 1.5 Large
- you need open weights you can self-host or fine-tune
At a glance
The differences that matter most.
Individual benchmarks
11 reported for Inkling 2 Small · 8 for Jamba 1.5 Large
Inkling 2 Small outperforms in 1 benchmarks (GPQA), while Jamba 1.5 Large is better at 0 benchmarks.
Inkling 2 Small 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, Inkling 2 Small ($0.30/1M tokens) is 6.7x cheaper than Jamba 1.5 Large ($2.00/1M tokens).
For output processing, Inkling 2 Small ($1.00/1M tokens) is 8.0x cheaper than Jamba 1.5 Large ($8.00/1M tokens).
In conclusion, Jamba 1.5 Large is more expensive than Inkling 2 Small.*
* Using a 3:1 ratio of input to output tokens
Model Size
Parameter count comparison
Inkling 2 Small has 196.0B more parameters than Jamba 1.5 Large, making it 49.2% larger.
Context Window
Maximum input and output token capacity
Both models have the same input context window of 256,000 tokens. Only Jamba 1.5 Large specifies output context (256,000 tokens).
Input capabilities
Documented input modalities across available providers
Inkling 2 Small supports multimodal inputs, whereas Jamba 1.5 Large does not.
Inkling 2 Small can handle both text and other forms of data like images, making it suitable for multimodal applications.
Inkling 2 Small
Jamba 1.5 Large
License
Usage and distribution terms
Inkling 2 Small 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
Inkling 2 Small was released on 2026-11-05, while Jamba 1.5 Large was released on 2024-08-22.
Inkling 2 Small is 27 months newer than Jamba 1.5 Large.
Nov 5, 2026
4 days ago
2.2yr newerAug 22, 2024
2.2 years ago
Knowledge Cutoff
When training data ends
Jamba 1.5 Large has a documented knowledge cutoff of 2024-03-05, while Inkling 2 Small'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 Inkling 2 Small's cutoff date.
—
Mar 2024
Provider Availability
Inkling 2 Small is available from Thinking Machines Lab. Jamba 1.5 Large is available from Bedrock, Google.
Inkling 2 Small
Jamba 1.5 Large
Outputs Comparison
Judge for yourself.
Run your own prompts against Inkling 2 Small and Jamba 1.5 Large side-by-side, then vote on the output you prefer.
FAQ
Common questions about Inkling 2 Small vs Jamba 1.5 Large.