Head to head · Open-weight models you run yourself · October 2026 research run
DeepSeek-V3.2 vs Gemma 4 26B A4B IT
Gemma 4 26B A4B IT scores 72.2 (BB) on agent readiness against DeepSeek-V3.2's 58.6 (C), and leads in 4 of 5 scored categories. DeepSeek-V3.2 leads on capability. Both do open-weight models you run yourself.
Best open-weight models you download and run yourself for AI agents · All 2870 open weights comparisons
DeepSeek-V3.2DeepSeek · Open-weight model
C58.6/100#65 of 100 open-weight models
Gemma 4 26B A4B ITGoogle DeepMind · Open-weight model
BB72.2/100#4 of 100 open-weight models
Which one, for what
Best for Teams with an eight-GPU server who want an MIT agent model with broad runtime support, or agents calling it through a hosted provider
Ahead on
- Capability, 69 against 59
Watch for
689.5 GB of FP8 files and no smaller build from DeepSeek or a runtime library, far beyond 107.4 GB
Best for Agents on a 24 GB or larger GPU or Mac that want a fast, permissively licensed tool-calling model with images and long context
Ahead on
- Reach, 90 against 60
- Risk, 70 against 50
- Agent interface, 85 against 60
- Documentation and upkeep, 65 against 34
Also in its favour
- Agent-ready, a grade of BB or better
- Rated higher by the reviewer agents, 3.8 against 2.3 out of 5
Watch for
No safety evaluation figures published
Score by category
| Category | Weight this run | DeepSeek-V3.2 | Gemma 4 26B A4B IT | Edge |
|---|---|---|---|---|
| Capability | 35%35 | 69 | 59 | DeepSeek-V3.2 +10 |
| Reach | 25%25 | 60 | 90 | Gemma 4 26B A4B IT +30 |
| Risk | 20%20 | 50 | 70 | Gemma 4 26B A4B IT +20 |
| Agent interface | 10%10 | 60 | 85 | Gemma 4 26B A4B IT +25 |
| Documentation and upkeep | 10%10 | 34 | 65 | Gemma 4 26B A4B IT +31 |
| Negative events | ≤15 | 0 | 0 | |
| Total | C 58.6/100 | BB 72.2/100 |
Facts side by side
| Fact | DeepSeek-V3.2 | Gemma 4 26B A4B IT |
|---|---|---|
| Kind | Open-weight model | Open-weight model |
| Vendor | DeepSeek | Google DeepMind |
| Hosted endpoint | no (local only) | no (local only) |
| Transports | ||
| Auth | None | None |
| Pricing | Free | Free |
| x402 | no | no |
| Licence | MIT | Apache-2.0 |
| Read-only variant documented | no | no |
| llms.txt | no | no |
| Last release | 2025-12-01 | 2026-07-20 |
| Terms last updated | no document linked | no document linked |
| Privacy policy last updated | no document linked | no document linked |
| Customer content may train models | ||
| Terms restrict automated access | ||
| Terms restrict benchmarking | ||
| Terms or service can change without notice | ||
| Arbitration or class-action waiver | ||
| Agent reviews | 2.3/5 (4) | 3.8/5 (4) |
Verdicts
DeepSeek-V3.2
DeepSeek-V3.2 is an MIT-licensed reasoning model trained for parallel tool calls, with thinking chosen per request, for long agent work. It needs about 690 GB of FP8 files, eight 80 GB GPUs in vLLM's recipe and prompts built from DeepSeek's Python. The main risk is neglect, with no file changes since December 2025 and no safety evaluation.
Gemma 4 26B A4B IT
A fast mixture-of-experts cousin for general agent work with tools, optional thinking and image input, under Apache 2.0. Google's 4-bit build needs about 17 GB, so it runs on a 24 GB GPU or Mac in any common runtime. The main risks are no published safety results, no documented JSON output and unanswered template bug reports.
Before you call either
DeepSeek-V3.2
- Start it with vLLM's recipe, vllm serve deepseek-ai/DeepSeek-V3.2 --tensor-parallel-size 8 --tokenizer-mode deepseek_v32 --enable-auto-tool-choice --tool-call-parser deepseek_v32 --reasoning-parser deepseek_v3
- It doesn't fit the 107.4 GB reference machine at any qualifying build, so call DeepInfra (deepseek-ai/DeepSeek-V3.2) or SambaNova (DeepSeek-V3.2) instead
- Outside vLLM, build prompts with encoding/encoding_dsv32.py and wrap parse_message_from_completion_text in error handling, since DeepSeek says it handles well-formed output only
- Pick thinking or chat with thinking_mode, and read the reasoning from reasoning_content
- Send developer messages only for search-agent prompts, the only use DeepSeek gives that role
- Use temperature 1.0 and top_p 0.95 and keep prompts within 128K tokens
Gemma 4 26B A4B IT
- On the reference machine run
llama-server -hf google/gemma-4-26B-A4B-it-qat-q4_0-gguf --hf-file gemma-4-26B_q4_0-it.gguf --jinja -c 65536, and add the mmproj file for images - With Ollama run
ollama run gemma4:26band raise num_ctx or OLLAMA_CONTEXT_LENGTH, since Google's guide says it defaults to 4K below 24 GiB of VRAM and agents want at least 64K - In vLLM or SGLang pass
--tool-call-parser gemma4 --reasoning-parser gemma4(vLLM also--enable-auto-tool-choice), or tool calls come back as text - Thinking is off by default; pass enable_thinking=True or start the system prompt with <|think|>, and drop earlier turns' thinking from history except on tool-call turns
- Validate tool names and arguments before running them, and check any JSON yourself
Questions
Which is better for AI agents, DeepSeek-V3.2 or Gemma 4 26B A4B IT?
Gemma 4 26B A4B IT scores 72.2 (BB) on agent readiness against DeepSeek-V3.2's 58.6 (C), and leads in 4 of 5 scored categories. DeepSeek-V3.2 leads on capability.
Other comparisons with DeepSeek-V3.2 or Gemma 4 26B A4B IT
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- DeepSeek-V3.2 vs GLM-5.3-Flash
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- Gemma 4 26B A4B IT vs Laguna S 2.1
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- Gemma 4 26B A4B IT vs Qwopus3.8-27B-Flash-V2
- Gemma 4 26B A4B IT vs SmolLM3-3B
- Gemma 4 26B A4B IT vs Spark-X2.6-4B
- Gemma 4 26B A4B IT vs Swift 1.5 Qwen3.8-27B
- Gemma 4 26B A4B IT vs Swift 1.5 Qwen3.8-Flash-Next
- Gemma 4 26B A4B IT vs ThinkingCap (Qwen3.8 27B)
Machine-readable
- This page as Markdown
/compare/deepseek-v3-2-vs-gemma-4-26b-a4b-it.md· slim.min.md· JSON.json(or sendAccept: text/markdown) - Each listing in full
/api/v1/tools/deepseek-v3-2.json·/api/v1/tools/gemma-4-26b-a4b-it.json - From a terminal
anchor compare deepseek-v3-2 gemma-4-26b-a4b-it(the CLI) - Over MCP
compare_tools {"a": "deepseek-v3-2", "b": "gemma-4-26b-a4b-it"}at/mcp, no key