Model vs model
Gemma 4 26B A4B IT vs Llama 4 Scout
Both are open-weight models that accept text and images. Gemma 4 26B A4B IT is the stronger choice: it scores 16.7 on the Intelligence Index against 6.5 for Llama 4 Scout, and costs slightly less at $0.26 per 1M tokens blended versus $0.29. It also wins every shared benchmark, with the widest gap on long documents, 65.7 against 27.7 on AA-LCR.
At a glance
Longer bar means the bigger number.
Blended price · per 1M tokens, lower is better
Intelligence Index · Artificial Analysis, higher is better
Context window · tokens it can read at once
Where they sit among all 44 models
Gemma 4 26B A4B IT ranks 34th of the 43 models with an Intelligence Index, Llama 4 Scout 43rd. Up and to the left is smarter for less money. Hover or tap a dot for its numbers, then click it to compare that model with Gemma 4 26B A4B IT.
Which one to pick
Pick Gemma 4 26B A4B IT if
- you want the lower bill ($0.26 vs $0.29 per 1M tokens blended)
- you want the higher overall score (16.7 vs 6.5)
- you care about AA-LCR (long documents): 65.7 vs 27.7
- you care about IFBench (instruction following): 72.4 vs 39.5
How smart is each one?
Of the 10 scores both models have, Gemma 4 26B A4B IT is ahead on every one.
Every intelligence score we have
Out of 100, higher is better. Gemma 4 26B A4B IT Llama 4 Scout
Overall
Intelligence Index
16.76.5
By area
Coding
39.38.2Math
14Gemma 4 26B A4B IT: not scored yet
Agentic work
2.90.5
By field of work
Finance & accounting
6.6Gemma 4 26B A4B IT: not scored yet
Legal
8.1Gemma 4 26B A4B IT: not scored yet
Economics
18.910.5Engineering
9.5Gemma 4 26B A4B IT: not scored yet
Strategy & operations
4.9Gemma 4 26B A4B IT: not scored yet
Benchmarks
Hard reasoning · Humanity's Last Exam
19.33.8Science · GPQA Diamond
79.258.7Knowledge · MMLU-Pro
75.2Gemma 4 26B A4B IT: not scored yet
Competitive coding · LiveCodeBench
29.9Gemma 4 26B A4B IT: not scored yet
Scientific coding · SciCode
4021.3Terminal tasks · Terminal-Bench 4.0
0Gemma 4 26B A4B IT: not scored yet
Tool use · τ-Bench Banking
123.3Instruction following · IFBench
72.439.5Long documents · AA-LCR
65.727.7Competition math · AIME 2025
14Gemma 4 26B A4B IT: not scored yet
Competition math · AIME 2024
28.3Gemma 4 26B A4B IT: not scored yet
Math · MATH-500
84.4Gemma 4 26B A4B IT: not scored yet
Uncensored · UGI
20.8Llama 4 Scout: not scored yet
The specs
| Spec | Gemma 4 26B A4B IT | Llama 4 Scout |
|---|---|---|
| Input price | $0.15 per 1M | $0.17 per 1M |
| Output price | $0.60 per 1M | $0.66 per 1M |
| Cost per test task | Not tested | Not tested |
| Context window | 262K | 128K |
| Max output | 131K | 8K |
| Accepts | Text, Image, PDF | Text, Image |
| Open weights | Yes | Yes |
| Lab | Meta | |
| Released | Apr 2026 | Apr 2025 |
| Knowledge cutoff | Not published | Aug 2024 |
Blended price weighs 3 parts input to 1 part output. List prices from models.dev.
Questions
Is Gemma 4 26B A4B IT or Llama 4 Scout cheaper?
Gemma 4 26B A4B IT is cheaper. It charges $0.15 per 1M input tokens and $0.60 per 1M output tokens, against $0.17 and $0.66 for Llama 4 Scout. Blended 3 parts input to 1 part output, that is $0.26 against $0.29 per 1M tokens.
Which is smarter, Gemma 4 26B A4B IT or Llama 4 Scout?
Gemma 4 26B A4B IT scores higher on the Artificial Analysis Intelligence Index, 16.7 against 6.5 for Llama 4 Scout. The biggest differences are on AA-LCR (65.7 vs 27.7) and IFBench (72.4 vs 39.5).
Which has the longer context window?
Gemma 4 26B A4B IT accepts 262K tokens, against 128K for Llama 4 Scout.
Can I compare Gemma 4 26B A4B IT and Llama 4 Scout on my own prompt?
Yes. ModelCompare sends one prompt to both models at once and shows each answer side by side with what it cost. You can try it free without an account.
Related comparisons
Where the numbers come from
Intelligence Index and benchmark scores are from Artificial Analysis, at each model’s highest effort, except SimpleQA, which is from Epoch AI, and the Uncensored score, which is from the UGI Leaderboard. The hallucination test is ours: questions with one exact answer, fake evidence pushed against right answers and requests that tempt a model to name packages that don’t exist. Prices are list prices from models.dev. Example answers come from our own test runs. Updated October 25, 2026.