Which license should you avoid for commercial use?

Avoid CC-BY-NC (Command R+), which prohibits commercial use. Read the Tencent Hunyuan, Pangu, DBRX, and Qwen License terms carefully; they contain specific restrictions and differ from Apache 2.0. Apache 2.0 and MIT remain the choices without legal friction.

What if I only have a single 80 GB GPU?

Target gpt-oss 120B (~70 GB Q4, Apache 2.0) or Llama 3.3 70B Instruct (~40 GB Q4). For reasoning, DeepSeek R1 Distill Llama 70B is documented in the official DeepSeek-R1 repository on GitHub. You will lose quality compared with Opus, but it will remain usable in production on a single node.

Best open-source alternative to Claude Opus 4.7

Q: What is the best direct substitute for Claude Opus 4.7?

By the criteria of raw quality plus a permissive license, DeepSeek V4 Pro 1.6T (MIT) and Mistral Large 3 675B (Apache 2.0) are the two best candidates. DeepSeek targets the top of the benchmark, while Mistral Large 3 offers a better VRAM/quality ratio and a European ecosystem.

Q: How much VRAM do you need to replace Opus locally?

For a level close to Opus, plan on 400 to 600 GB of VRAM in Q4. That means 5 to 8 80 GB H100 GPUs. For acceptable use below that level, Qwen 3 235B-A22B at 142 GB fits on 2 GPUs. Use the configurator to size your setup.

Q: Which model for coding as an Opus replacement?

Qwen3-Coder-Next 80B-A3B (Apache 2.0) fits in 48 GB in Q4 and achieves competitive HumanEval scores. For higher quality, DeepSeek V3.2 remains strong on SWE-bench (exact figures to be confirmed on your repositories). More details in best LLM for coding.

Search for a Claude Opus alternative in open weights meets three concrete needs: controlling inference costs, keeping your data on-site, and freely auditing the weights. This page compares the best candidates for replacing Claude Opus 4.7 with a self-hosted model, including real VRAM specs, production-ready licenses, and use cases. It covers “frontier” models (≥ 600 GB of VRAM in Q4), reasonable mid-range options on a standard GPU node, specialized profiles (code, vision, reasoning), and then how to choose based on your hardware.

Why replace Claude with an open-weights model

Anthropic does not publish its weights. Any migration to self-hosting therefore means switching model families. Three motivations come up repeatedly: privacy (medical, legal, and defense data), software sovereignty (auditable weights, internal fine-tuning), and savings at high volumes. A credible replacement must meet three criteria: a permissive license (Apache 2.0, MIT, or an equivalent license allowing commercial use), long-form reasoning capability, and a mature inference ecosystem (vLLM, SGLang, llama.cpp).

Our quantization guide details the Q4/Q5/Q8/FP16 tiers and their impact on quality. As a reference point, moving from FP16 to Q4 cuts VRAM by about 3.5×—a decisive factor in making these models accessible outside data centers. For budget tradeoffs, also see how much a self-hosted LLM costs.

Useful external sources for validating the figures presented here: the Hugging Face Open LLM Leaderboard, the reference repo vLLM for tokens/sec benchmarks, and thearXiv index on MoE to understand the sparse architecture that dominates this category.

The frontier: replacing Opus while keeping top-tier quality

To reach Opus 4.7’s quality level, start by looking at MoE models with more than 600 billion parameters activated. These are the only serious candidates for complex reasoning.

DeepSeek V4 Pro 1.6T : 1600B parameters, MIT license, 1M-token context. Q4 VRAM ≈ 960 GB, equivalent to 12 × H100 80 GB or 6 × H200. MoE architecture with sparse activation; HumanEval and AIME scores announced as very close to the proprietary leader (to be confirmed on your internal prompts). MIT license = complete freedom for commercial use.
MiMo V2.5 Pro (Xiaomi, 1020B, MIT): Q4 VRAM ≈ 595 GB, 1M context. General-purpose profile, context window comparable to Opus.
Kimi K2.6 (Moonshot AI, 1000B, Modified MIT): Q4 VRAM ≈ 600 GB, 256k context. K2.6 addresses several limitations of Kimi K2.5 for multi-turn instruction following.
Ring-1T and Ling 2.6 1T (Ant Group, MIT): two 1T models focused respectively on reasoning (Ring) and general-purpose tasks (Ling). Both can be used without commercial restrictions.

The comparison Kimi vs DeepSeek details the practical differences for long-context RAG.

The reasonable tier: 400–700B in MoE

This tier offers the best quality-to-VRAM ratio for most teams. An 8 × H100 80 GB node (640 GB of usable VRAM) can run Q4 for most of these models.

GLM-5.1 (Z.AI, 744B, MIT): ~445 GB Q4 VRAM, 200k context. Excellent Chinese/English bilingual profile, solid code quality. Also see its previous version GLM 5 744B-A40B.
Mistral Large 3 675B (Apache 2.0): 405 GB in Q4, 256k context. The most credible French candidate against Opus, with an Apache 2.0 license without caveats and a mature ecosystem.
DeepSeek R1 671B : explicit reasoning model (integrated chain of thought). MIT, 128k context, formidable AIME/MATH profile according to the DeepSeek-R1 paper on arXiv.
DeepSeek V3.2 : 685B, MIT, Q4 VRAM ≈ 410 GB. More general-purpose than R1, better for chat and instruct.
Rakuten AI 3.0 : 700B Apache 2.0, 32k context only—reserved for short use cases.

For a broader comparison, see best 400B-700B LLMs.

Tokens/sec and actual inference cost

The figures below are observed ballpark figures using vLLM 0.7+ and SGLang on 8 × H100, batch 1, Q4 quantization (to be confirmed for your stack):

Mistral Large 3 675B : ~28–35 tok/s during generation.
DeepSeek V3 671B : ~30–40 tok/s; MoE activation (37B active) greatly accelerates batching.
Llama 3.1 405B Instruct : dense model, slower, ~18–25 tok/s but predictable quality and a huge ecosystem.
Qwen 3 235B-A22B : 142 GB Q4 VRAM, fits on 2 × H100s, ~50–70 tok/s — the best compromise if you don’t have a cluster.

The official documentation for vLLM on MoEs confirms that sparse architectures benefit significantly from continuous batching. For a data-driven comparison, see tokens/sec H100 vs MI300X.

Specialized profiles: code, vision, reasoning

If you’re replacing Opus for a specific use case, target the profile rather than raw size.

Code : Qwen3-Coder-Next 80B-A3B (Apache 2.0, 48 GB in Q4) achieves competitive HumanEval/SWE-bench scores against Opus on short tasks. See best LLM for coding.
Reasoning : DeepSeek R1 671B or its distilled version DeepSeek R1 Distill Llama 70B (40 GB Q4, fits on 1 × A100 80 GB).
Vision: Qwen 3 VL 235B-A22B and Qwen 2.5 VL 72B cover multimodal use cases. Molmo 72B (Apache 2.0, Allen AI) is documented in detail in the Molmo paper.
Ultra-long context : Llama 4 Scout 109B claims 10 M context tokens (to be confirmed on your data).
Small single-GPU server : gpt-oss 120B (Apache 2.0, OpenAI), 70 GB Q4. Fits on 1 × H100 80 GB.

For vision only, see best multimodal LLM.

Licenses: what really deploys in production

An Anthropic alternative A credible [model] must have a license that raises no questions for your legal team. Ranked from most to least permissive:

Apache 2.0 / MIT : free commercial use, redistribution permitted. This notably applies to Mistral Large 3, Mixtral 8x22B Instruct, Qwen 3 235B-A22B, gpt-oss 120B, the entire DeepSeek family (except base V3), MiMo, Ring, Ling, GLM-5.1.
Llama Community (3.x and 4.x): commercial use permitted below the threshold of 700 M MAU. Sufficient for 99% of projects. Applies to Llama 3.3 70B Instruct, Llama 3.1 405B, Llama 4 Maverick 400B.
Restrictive vendor licenses : Command R+ 104B (CC-BY-NC, non-commercial), DBRX Instruct (Databricks Open Model License), Tencent, and Pangu. Read before deployment.

The clause-by-clause details are in open-weight licensing guide.

FAQ

Q: What is the best direct substitute for Claude Opus 4.7?

By the criterion of raw quality + permissive license, DeepSeek V4 Pro 1.6T (MIT) and Mistral Large 3 675B (Apache 2.0) are the two best candidates. DeepSeek targets the top of the benchmark, while Mistral Large 3 offers a better VRAM/quality ratio and a European ecosystem.

Q: Can Claude be self-hosted?

No. Anthropic does not distribute Claude's weights. Any so-called “Claude self-hosted” solution is actually a deployment of a different open-weights model (DeepSeek, Qwen, Llama, Mistral) with a system prompt that imitates the style. The real question is: which open-weights model meets your requirements?

Q: How much VRAM do you need to replace Opus locally?

For Opus-level performance, plan on 400 to 600 GB of VRAM in Q4. That means 5 to 8 H100 80 GB GPUs. For acceptable but lower-tier use, Qwen 3 235B-A22B at 142 GB fits on 2 GPUs. Use the configurator for sizing.

Q: Which license should be avoided for commercial use?

Avoid CC-BY-NC (Command R+) which prohibits commercial use. Read the Tencent Hunyuan, Pangu, DBRX, and Qwen License licenses carefully (different from Apache 2.0), as they contain specific restrictions. Apache 2.0 and MIT remain the choices with no legal friction.

Q: Which model should replace Opus for coding?

Qwen3-Coder-Next 80B-A3B (Apache 2.0) fits in 48 GB in Q4 and achieves competitive HumanEval scores. For higher quality, DeepSeek V3.2 remains strong on SWE-bench (exact figures to be confirmed on your repositories). More details in best LLM for coding.

Q: What if I only have one 80 GB GPU?

Aim for gpt-oss 120B (~70 GB Q4, Apache 2.0) or Llama 3.3 70B Instruct (~40 GB Q4). For reasoning, DeepSeek R1 Distill Llama 70B is documented in the official repo DeepSeek-R1 on GitHub. You'll lose quality compared with Opus, but it will remain usable in production on a single node.

Conclusion

The right choice ofClaude Opus alternative depends on your available VRAM and license tolerance. With a high GPU budget, target DeepSeek V4 Pro or Mistral Large 3. At an intermediate budget, Qwen 3 235B-A22B or gpt-oss 120B offer a solid compromise. To size your deployment precisely and compare them side by side, open the BestLLMfor configurator or browse the complete catalog of the 249 models.