DeepSeek-V4.1-Flash-1017 vs IBM Granite 4.2 30B
DeepSeek-V4.1-Flash-1017 leads the LLM Stats Score 51.2 to 23.3. IBM Granite 4.2 30B is 1.9x cheaper per token.
DeepSeek · IBM · Updated for 2026
Which is better?
DeepSeek-V4.1-Flash-1017 leads the overall LLM Stats Score 51.2 to 23.3, ranking #15 overall.
On price, IBM Granite 4.2 30B is roughly 1.9x cheaper per token on a blended 3:1 input/output basis, which adds up quickly at production volume.
DeepSeek-V4.1-Flash-1017 also accepts a larger context window (1,048,576 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 DeepSeek-V4.1-Flash-1017
- overall performance matters — it scores 51.2 and ranks #15 on LLM Stats
- your work emphasizes reasoning and coding — it leads those capability indexes
- you process long inputs — it offers a 1,048,576 token context window
- you want the most recent training data — it shipped Oct 2026
Choose IBM Granite 4.2 30B
- cost matters — it's about 1.9x cheaper per token
At a glance
The differences that matter most.
Capability indexes
Additional strengths measured across groups of related public benchmarks
Individual benchmarks
11 reported for DeepSeek-V4.1-Flash-1017 · 18 for IBM Granite 4.2 30B
DeepSeek-V4.1-Flash-1017 and IBM Granite 4.2 30Bdon'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, DeepSeek-V4.1-Flash-1017 ($0.30/1M tokens) is 1.9x more expensive than IBM Granite 4.2 30B ($0.16/1M tokens).
For output processing, DeepSeek-V4.1-Flash-1017 ($1.20/1M tokens) is 1.8x more expensive than IBM Granite 4.2 30B ($0.65/1M tokens).
In conclusion, DeepSeek-V4.1-Flash-1017 is more expensive than IBM Granite 4.2 30B.*
* Using a 3:1 ratio of input to output tokens
Model Size
Parameter count comparison
DeepSeek-V4.1-Flash-1017 has 733.2B more parameters than IBM Granite 4.2 30B, making it 2444.0% larger.
Context Window
Maximum input and output token capacity
DeepSeek-V4.1-Flash-1017 accepts 1,048,576 input tokens compared to IBM Granite 4.2 30B's 131,072 tokens. DeepSeek-V4.1-Flash-1017 can generate longer responses up to 393,216 tokens, while IBM Granite 4.2 30B is limited to 131,072 tokens.
Input capabilities
Documented input modalities across available providers
DeepSeek-V4.1-Flash-1017 supports multimodal inputs, whereas IBM Granite 4.2 30B does not.
DeepSeek-V4.1-Flash-1017 can handle both text and other forms of data like images, making it suitable for multimodal applications.
DeepSeek-V4.1-Flash-1017
IBM Granite 4.2 30B
License
Usage and distribution terms
DeepSeek-V4.1-Flash-1017 is licensed under MIT, while IBM Granite 4.2 30B uses Apache 2.0.
License differences may affect how you can use these models in commercial or open-source projects.
MIT
Open weights
Apache 2.0
Open weights
Release Timeline
When each model was launched
DeepSeek-V4.1-Flash-1017 was released on 2026-10-17, while IBM Granite 4.2 30B was released on 2026-08-25.
DeepSeek-V4.1-Flash-1017 is 2 months newer than IBM Granite 4.2 30B.
Oct 17, 2026
3 days ago
1mo newerAug 25, 2026
1 months ago
Knowledge Cutoff
When training data ends
Neither model specifies a knowledge cutoff date.
Unable to compare the recency of their training data.
Provider Availability
DeepSeek-V4.1-Flash-1017 is available from DeepSeek. IBM Granite 4.2 30B is available from DeepInfra.
DeepSeek-V4.1-Flash-1017
IBM Granite 4.2 30B
Outputs Comparison
Judge for yourself.
Run your own prompts against DeepSeek-V4.1-Flash-1017 and IBM Granite 4.2 30B side-by-side, then vote on the output you prefer.
FAQ
Common questions about DeepSeek-V4.1-Flash-1017 vs IBM Granite 4.2 30B.