Jamba 1.5 Large vs Ling 3.0 Flash Fin
Ling 3.0 Flash Fin leads the LLM Stats Score 43.3 to 1.0. Ling 3.0 Flash Fin is 38.9x cheaper per token.
AI21 Labs · InclusionAI · Updated for 2026
Which is better?
Ling 3.0 Flash Fin leads the overall LLM Stats Score 43.3 to 1.0, ranking #39 overall.
On price, Ling 3.0 Flash Fin is roughly 38.9x cheaper per token on a blended 3:1 input/output basis, which adds up quickly at production volume.
Ling 3.0 Flash Fin also accepts a larger context window (262,144 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 Jamba 1.5 Large
- you need open weights you can self-host or fine-tune
Choose Ling 3.0 Flash Fin
- overall performance matters — it scores 43.3 and ranks #39 on LLM Stats
- your work emphasizes reasoning — it leads those capability indexes
- cost matters — it's about 38.9x cheaper per token
- you process long inputs — it offers a 262,144 token context window
- you want the most recent training data — it shipped Sep 2026
At a glance
The differences that matter most.
Capability indexes
Additional strengths measured across groups of related public benchmarks
Individual benchmarks
8 reported for Jamba 1.5 Large · 6 for Ling 3.0 Flash Fin
Jamba 1.5 Large and Ling 3.0 Flash Findon't have any common benchmark datasets to compare. They may have been evaluated on different testing suites.
Human preference
Blind head-to-head votes and playground preference scores
Pricing Analysis
Price comparison per million tokens
For input processing, Jamba 1.5 Large ($2.00/1M tokens) is 33.3x more expensive than Ling 3.0 Flash Fin ($0.06/1M tokens).
For output processing, Jamba 1.5 Large ($8.00/1M tokens) is 44.4x more expensive than Ling 3.0 Flash Fin ($0.18/1M tokens).
In conclusion, Jamba 1.5 Large is more expensive than Ling 3.0 Flash Fin.*
* Using a 3:1 ratio of input to output tokens
Model Size
Parameter count comparison
Jamba 1.5 Large has 274.0B more parameters than Ling 3.0 Flash Fin, making it 221.0% larger.
Context Window
Maximum input and output token capacity
Ling 3.0 Flash Fin accepts 262,144 input tokens compared to Jamba 1.5 Large's 256,000 tokens. Ling 3.0 Flash Fin can generate longer responses up to 262,144 tokens, while Jamba 1.5 Large is limited to 256,000 tokens.
Release Timeline
When each model was launched
Jamba 1.5 Large was released on 2024-08-22, while Ling 3.0 Flash Fin was released on 2026-09-03.
Ling 3.0 Flash Fin is 25 months newer than Jamba 1.5 Large.
Aug 22, 2024
2.0 years ago
Sep 3, 2026
5 days ago
2.0yr newerKnowledge Cutoff
When training data ends
Jamba 1.5 Large has a documented knowledge cutoff of 2024-03-05, while Ling 3.0 Flash Fin'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 Ling 3.0 Flash Fin's cutoff date.
Mar 2024
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Provider Availability
Jamba 1.5 Large is available from Bedrock, Google. Ling 3.0 Flash Fin is available from DeepInfra.
Jamba 1.5 Large
Ling 3.0 Flash Fin
Outputs Comparison
Judge for yourself.
Run your own prompts against Jamba 1.5 Large and Ling 3.0 Flash Fin side-by-side, then vote on the output you prefer.
FAQ
Common questions about Jamba 1.5 Large vs Ling 3.0 Flash Fin.