DeepSeek-V4.1-Flash vs IBM Granite 4.2 30B
DeepSeek-V4.1-Flash leads the LLM Stats Score 51.5 to 23.8. IBM Granite 4.2 30B is 1.2x cheaper per token.
DeepSeek · IBM · Updated for 2026
Which is better?
DeepSeek-V4.1-Flash leads the overall LLM Stats Score 51.5 to 23.8, ranking #13 overall.
In the 2 individual benchmarks reported for both models, DeepSeek-V4.1-Flash wins 2; this is a narrower head-to-head signal than the composite indexes.
On price, IBM Granite 4.2 30B is roughly 1.2x cheaper per token on a blended 3:1 input/output basis, which adds up quickly at production volume.
DeepSeek-V4.1-Flash also accepts a larger context window (1,040,000 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 DeepSeek-V4.1-Flash
- overall performance matters — it scores 51.5 and ranks #13 on LLM Stats
- your work emphasizes reasoning and coding — 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 1,040,000 token context window
- you want the most recent training data — it shipped Sep 2026
Choose IBM Granite 4.2 30B
- cost matters — it's about 1.2x cheaper per token
At a glance
The differences that matter most.
Capability indexes
Additional strengths measured across groups of related public benchmarks
Individual benchmarks
20 reported for DeepSeek-V4.1-Flash · 18 for IBM Granite 4.2 30B
DeepSeek-V4.1-Flash outperforms in 2 benchmarks (GPQA, Terminal-Bench 2.1), while IBM Granite 4.2 30B is better at 0 benchmarks.
DeepSeek-V4.1-Flash 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, DeepSeek-V4.1-Flash ($0.22/1M tokens) is 1.4x more expensive than IBM Granite 4.2 30B ($0.16/1M tokens).
For output processing, DeepSeek-V4.1-Flash ($0.66/1M tokens) is 1.0x more expensive than IBM Granite 4.2 30B ($0.65/1M tokens).
In conclusion, DeepSeek-V4.1-Flash 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 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 accepts 1,040,000 input tokens compared to IBM Granite 4.2 30B's 131,072 tokens. DeepSeek-V4.1-Flash 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 supports multimodal inputs, whereas IBM Granite 4.2 30B does not.
DeepSeek-V4.1-Flash can handle both text and other forms of data like images, making it suitable for multimodal applications.
DeepSeek-V4.1-Flash
IBM Granite 4.2 30B
License
Usage and distribution terms
DeepSeek-V4.1-Flash 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 was released on 2026-09-10, while IBM Granite 4.2 30B was released on 2026-08-25.
DeepSeek-V4.1-Flash is 1 month newer than IBM Granite 4.2 30B.
Sep 10, 2026
1 weeks ago
2w newerAug 25, 2026
4 weeks 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 is available from Fireworks, DeepInfra, DeepSeek, Novita. IBM Granite 4.2 30B is available from DeepInfra.
DeepSeek-V4.1-Flash
IBM Granite 4.2 30B
Outputs Comparison
Judge for yourself.
Run your own prompts against DeepSeek-V4.1-Flash and IBM Granite 4.2 30B side-by-side, then vote on the output you prefer.
FAQ
Common questions about DeepSeek-V4.1-Flash vs IBM Granite 4.2 30B.