IBM Granite 4.1 and Devstral Small 24B represent two different philosophies for open-source AI. Granite is an enterprise-first model family with language, vision, speech, and safety models under one roof. Devstral is a coding specialist — built by Mistral specifically for software engineering tasks, optimized for agentic workflows, and laser-focused on code quality.
Both are dense transformers. Both are Apache 2.0. Both run on consumer hardware. The choice comes down to what you are building: a broad enterprise AI platform or a dedicated coding tool.
For full details on Granite 4.1’s architecture and benchmarks, see the Granite 4.1 complete guide.
Quick verdict
Pick Granite 4.1 if you need a versatile model family with tool calling, 512K context, vision capabilities, safety guardrails, and enterprise compliance features. The 8B model is the efficiency champion — 5 GB VRAM, 87.2 HumanEval. The 30B leads open-source tool calling.
Pick Devstral Small 24B if your primary use case is coding and you want the best code quality in a single mid-size model. Devstral is purpose-built for software engineering with strong SWE-bench scores and agentic coding capabilities. It trades breadth for depth.
Specifications compared
| Spec | Granite 4.1 8B | Granite 4.1 30B | Devstral Small 24B |
|---|---|---|---|
| Parameters | 8B | 30B | 24B |
| Architecture | Dense decoder-only | Dense decoder-only | Dense decoder-only |
| Context window | 512K | 512K | 256K |
| License | Apache 2.0 | Apache 2.0 | Apache 2.0 |
| VRAM (FP16) | ~16 GB | ~60 GB | ~48 GB |
| VRAM (Q4) | ~5 GB | ~18 GB | ~14 GB |
| VRAM (FP8) | ~8 GB | ~30 GB | ~24 GB |
| Training tokens | ~15T | ~15T | Not disclosed |
| Focus | General + enterprise | General + enterprise | Coding + agentic |
| Model family | Language, Vision, Speech, Guardian, Embedding | Same | Language only |
| Cryptographic signing | Yes | Yes | No |
| ISO certified | Yes | Yes | No |
The most striking difference: Granite 4.1 is a family of models (language, vision, speech, safety, embeddings) while Devstral Small is a single coding-focused model. If you need vision processing, speech transcription, or safety guardrails, Granite is the only option.
Benchmark comparison
Coding benchmarks
| Benchmark | Granite 4.1 8B | Granite 4.1 30B | Devstral Small 24B |
|---|---|---|---|
| HumanEval (pass@1) | 87.2 | 89.63 | ~85–88 |
| EvalPlus (coding) | 80.2 | 82.7 | ~80–84 |
| SWE-bench Verified | — | — | 68.0% |
Devstral Small 24B’s headline number is its 68.0% on SWE-bench Verified — a benchmark that measures real-world software engineering ability by testing whether a model can fix actual GitHub issues. This is a strong score for a 24B model and reflects Mistral’s focus on agentic coding workflows.
Granite 4.1 does not have published SWE-bench scores, which makes direct comparison on agentic coding difficult. On standard coding benchmarks (HumanEval, EvalPlus), the models are competitive, with Granite 4.1 30B having a slight edge.
General benchmarks
| Benchmark | Granite 4.1 8B | Granite 4.1 30B | Devstral Small 24B |
|---|---|---|---|
| MMLU (5-shot) | 73.84 | 80.16 | ~72–76 |
| GSM8K (8-shot) | 92.49 | 94.16 | ~85–90 |
| IFEval Avg | 87.06 | 89.65 | ~82–86 |
| BFCL V3 (tool calling) | 68.27 | 73.68 | ~55–62 |
| ArenaHard | 68.98 | 71.02 | ~65–70 |
Granite leads on general benchmarks, especially tool calling (BFCL V3) and instruction following (IFEval). This reflects IBM’s broader training focus — Granite is not just a coding model, it is a general-purpose model that happens to be good at coding.
Devstral’s strength is specifically in agentic software engineering — the kind of multi-step, tool-using, file-editing workflows that SWE-bench measures. If your use case looks like “fix this bug in a real codebase,” Devstral is optimized for exactly that.
Architecture: dense vs dense
Both models use dense decoder-only transformer architectures. No Mixture-of-Experts, no sparse routing. This means:
- Predictable latency — Every token costs the same compute. No routing variance.
- Simple deployment — No expert parallelism needed. Standard tensor parallelism works.
- Linear scaling — Memory and compute scale linearly with parameter count.
The architectural similarity means the comparison comes down to training data, training methodology, and alignment — not fundamental design differences.
IBM’s training approach is well-documented: five-phase progressive data annealing on 15 trillion tokens, LLM-as-Judge data filtering, four-stage RL pipeline with math recovery. Mistral’s training details for Devstral are less public, but their track record with Mistral Large and Codestral suggests a strong coding-focused data pipeline.
Context window: 512K vs 256K
Granite 4.1 supports 512K tokens (8B and 30B models). Devstral Small supports 256K tokens. Both are generous by 2026 standards.
| Context length | Granite 4.1 | Devstral Small 24B |
|---|---|---|
| 32K | Supported | Supported |
| 64K | Supported | Supported |
| 128K | Supported | Supported |
| 256K | Supported | Maximum |
| 512K | Maximum | Not supported |
For most coding tasks, 128K is more than enough. The difference matters when:
- Processing entire large codebases — A medium-to-large project can exceed 256K tokens. Granite handles it in one pass; Devstral requires chunking.
- Long document analysis — Legal contracts, research papers, or technical specifications that exceed 256K tokens.
- Extended conversations — Very long multi-turn sessions that accumulate context.
IBM’s staged context extension (32K → 128K → 512K with model merging) preserves short-context quality. Granite scores 83.6 on RULER at 32K and degrades gracefully to 73.0 at 128K. Devstral’s long-context quality is less documented but generally strong within its 256K range.
Enterprise features
This is where Granite 4.1 pulls ahead significantly. IBM built an enterprise trust stack around Granite:
Granite Guardian models
Separate safety models that evaluate inputs and outputs for:
- Harmful content
- Bias and fairness issues
- Policy compliance
- Hallucination detection
These run alongside the main model and can block or flag problematic content before it reaches users. Devstral has no equivalent — you would need to add a separate safety layer.
Granite Vision (4B)
Granite 4.1 Vision processes images and documents. It scores 86.5 on table extraction, beating Claude Opus 4.6 at 83.8. Use cases:
- Document OCR and structured data extraction
- Image understanding for multimodal applications
- Chart and diagram interpretation
Devstral is text-only. If your application needs to process images, you need a separate vision model.
Granite Speech (2B)
Granite 4.1 Speech handles speech-to-text with 5.33% word error rate. Useful for:
- Meeting transcription
- Voice-driven coding assistants
- Customer service automation
Again, Devstral offers no speech capability.
Granite Embedding models
Support 200+ languages for RAG pipelines and semantic search. Having embeddings from the same model family can improve retrieval quality for Granite-based applications.
Compliance and certification
- Cryptographic signing — Every Granite 4.1 model is cryptographically signed, ensuring you can verify the weights have not been tampered with.
- ISO certified AI Management System — IBM’s development process is ISO certified.
- AI Risk Atlas integration — Built-in risk assessment and mitigation frameworks.
Devstral has none of these enterprise compliance features. For regulated industries (finance, healthcare, government), Granite’s trust stack can be the deciding factor.
Coding focus: where Devstral wins
Devstral Small 24B is purpose-built for software engineering. Its advantages:
SWE-bench performance
68.0% on SWE-bench Verified means Devstral can fix real bugs in real codebases — not just generate isolated functions. This benchmark tests the full software engineering workflow: understanding an issue, navigating a codebase, identifying the right files, and making correct changes.
Agentic coding workflows
Devstral is optimized for multi-step coding tasks:
- Read a codebase
- Identify relevant files
- Plan changes across multiple files
- Execute edits
- Verify the result
This is the workflow that coding agents (like Cursor, Continue, and Aider) use. Devstral’s training specifically targets this pattern.
Mistral ecosystem
Devstral integrates with Mistral’s broader ecosystem:
- Le Chat (Mistral’s chat interface)
- Mistral API with function calling
- Codestral for code completion
If you are already in the Mistral ecosystem, Devstral fits naturally.
For a detailed look at Devstral Small, see the Devstral Small 2 guide.
Hardware comparison
Running the 8B Granite vs 24B Devstral
| Setup | Granite 4.1 8B (Q4) | Devstral Small 24B (Q4) |
|---|---|---|
| VRAM needed | ~5 GB | ~14 GB |
| RTX 3060 12GB | Runs well | Does not fit well |
| RTX 4060 8GB | Runs well | Does not fit |
| RTX 4090 24GB | Runs with room to spare | Runs with limited context |
| Mac 16GB | Runs well | Tight, limited context |
| Mac 32GB | Runs with full context | Runs well |
| Inference speed | ~40–60 tok/s (RTX 4060) | ~15–25 tok/s (RTX 4090) |
Granite 4.1 8B runs on hardware that Devstral cannot touch. If you have a mid-range GPU or a base-model Mac, Granite is your only option between these two.
Running the 30B Granite vs 24B Devstral
| Setup | Granite 4.1 30B (Q4) | Devstral Small 24B (Q4) |
|---|---|---|
| VRAM needed | ~18 GB | ~14 GB |
| RTX 4090 24GB | Fits | Fits with more context room |
| Mac 32GB | Fits | Fits |
| Inference speed | ~20–35 tok/s (RTX 4090) | ~25–40 tok/s (RTX 4090) |
At the 30B vs 24B level, Devstral is slightly more efficient — fewer parameters means less memory and faster inference. But Granite 30B brings 512K context, 73.68 BFCL V3 tool calling, and the full enterprise ecosystem.
When to pick Granite 4.1
Pick the 8B when:
- You have limited hardware (8 GB VRAM or less)
- You need the fastest possible inference
- Tool calling is a primary use case
- You want 512K context
- You are building a general-purpose assistant that also codes
- Enterprise compliance matters (cryptographic signing, ISO, Guardian)
- You need vision or speech capabilities alongside language
Pick the 30B when:
- You have 24+ GB VRAM and want maximum Granite quality
- Tool calling is critical (73.68 BFCL V3 — best in class)
- You need the full enterprise model family
- 512K context is a requirement
- You are deploying in regulated industries
When to pick Devstral Small 24B
- Pure coding focus — If your only use case is writing, reviewing, and fixing code, Devstral’s SWE-bench performance (68.0%) reflects real-world coding ability.
- Agentic workflows — If you are building or using coding agents that navigate codebases, plan multi-file changes, and execute edits, Devstral is optimized for this pattern.
- Mistral ecosystem — If you are already using Mistral models and tools.
- Mid-range hardware — At 24B parameters (Q4 ~14 GB), Devstral fits on an RTX 4090 with room for context, while Granite 30B is tighter.
- You do not need vision, speech, or safety models — If your application is purely text-based coding, Devstral’s focused approach avoids the complexity of a full model family.
Can you use both?
Yes. A practical setup:
- Granite 4.1 8B as your default model for chat, tool calling, general tasks, and quick coding questions
- Devstral Small 24B for complex coding sessions — multi-file refactoring, bug fixing in large codebases, agentic workflows
With Ollama, switching is trivial:
ollama run granite4.1:8b # General use, tool calling
ollama run devstral-small:24b # Deep coding sessionsThis gives you the best of both worlds: Granite’s efficiency and breadth for everyday tasks, Devstral’s coding depth for complex engineering work.
For a broader comparison of models you can run locally, see our best Ollama models for coding in 2026.
FAQ
Is Granite 4.1 8B good enough for coding compared to Devstral Small 24B?
For most coding tasks, yes. Granite 4.1 8B scores 87.2 on HumanEval and 80.2 on EvalPlus — competitive with models 3× its size. The gap shows up on complex agentic coding tasks (multi-file bug fixes, codebase navigation) where Devstral’s 68.0% SWE-bench score reflects specialized training. For code generation, code review, and standard development tasks, Granite 8B delivers excellent quality at a fraction of the hardware cost.
Which model is better for function calling and tool use?
Granite 4.1, by a significant margin. The 30B scores 73.68 on BFCL V3 and the 8B scores 68.27. Devstral’s tool-calling capabilities are less documented but estimated around 55–62 on the same benchmark. IBM specifically optimized Granite for enterprise tool-calling workflows. If your application involves calling APIs, querying databases, or orchestrating tools based on natural language, Granite is the clear choice.
Can Devstral Small 24B handle non-coding tasks?
Yes, but it is not optimized for them. Devstral can answer general questions, summarize text, and follow instructions, but its training prioritizes coding quality. For a model that handles both coding and general tasks well, Granite 4.1 is the better all-rounder. Devstral is best used as a dedicated coding model alongside a general-purpose model for other tasks.
How do the context windows compare for real coding work?
Granite’s 512K context holds roughly 1.5–2 million characters of code. Devstral’s 256K holds about 750K–1 million characters. For a typical coding session (working on a few files), both are more than enough. The difference matters when you need to load an entire large codebase into context — a monorepo with hundreds of files might exceed 256K tokens but fit within 512K. For most developers, 256K is sufficient, and the context window should not be the deciding factor.
Which model should I pick for a startup building a coding assistant?
Granite 4.1 8B for most startups. It runs on cheap hardware ($0.20–0.30/hour cloud GPUs), serves more concurrent users per GPU (smaller model = more throughput), supports tool calling natively, and the Apache 2.0 license has no restrictions. The 512K context handles large codebases. Use Devstral Small 24B only if your product specifically targets complex agentic coding workflows (like an AI that autonomously fixes bugs across repositories) and you can afford the 3× hardware cost per user.
Do I need the Granite Guardian models?
For production applications serving end users, yes. Guardian models add a safety layer that catches harmful content, bias, and policy violations before they reach users. This is especially important in enterprise and regulated environments. If you are building a personal coding assistant for your own use, Guardian models are optional. If you are building a product, they are a significant advantage over Devstral, which has no built-in safety layer.