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Best OpenClaw Model Alternatives to Opus 4.6

best openclaw models and alternatives to opus

Opus 4.6 is insanely expensive. Here’s a rundown of new models that offer comparable agency for OpenClaw at a fraction of the cost in March 2026.

Updated 23/03/2026

Deployment challenges for autonomous agents include not only raw reasoning capacity, but economic viability. For OpenClaw users, Anthropic’s Claude Opus 4.6 is a premium option, but their terms of service bar users from connecting via OAuth so API usage is a must. Pricing starts at $5.00 per million input tokens and $25.00 per million output tokens. For prompts in excess of 200K tokens on the Claude Developer Platform, long-context pricing applies ($10.00 input, $37.50 output). The result is the same either way. Opus is easy to love and even easier to accidentally overspend on in multi-turn agent sessions.

Editorial note

This report was originally compiled with the help of Manus and Gemini 3 Pro in research mode. GPT-5.3 was used in v1 to cross-check references, heavy editing and recurring updates by me. With the exception of MiMo-V2-Flash (I’m currently tapping Gemini Flash for routine heartbeats), I have used all of the models mentioned here with my OpenClaw agents.

Feel free to reach out to me on X.

Updated Model Pricing Comparison

The following table reflects current API rates as of 23 March 2026. In OpenClaw, per-session cost is mostly a context-management problem. Compaction strategy, memory flushing, and how much “garbage context” you keep dragging forward will swing token burn by multiples, even on the same task or in a quick chat that seems straightforward.

I’d treat the numbers below as raw rates. Your bill will depend on routing and hygiene. If you’re not regularly compacting, pruning tool output and flushing stale memory, Opus 4.6 will punish you with a severely high API invoice.

ModelInput ($ / MTok)Output ($ / MTok)Context WindowKey Cost Factor
Claude Opus 4.6$5.00/$10.00$25.00/$37.50<200K/<1MFrontier pricing, not for cron jobs 😉
Claude Sonnet 4.6$3.00/$6.00$15.00/$22.50200K – 1M40% savings over Opus on base token rates
GPT-5.4$2.50/$15.00$5.00 / $22.501MOpenAI’s flagship model 🔥
GPT-5.4 Mini$0.75$4.50400kNew option for subagents, coding helpers
GPT-5.4 Nano$0.20$1.25400kAPI-only utility tier for simple tasks
GPT-5.3 Instant$1.75$14.00128kLegacy low-cost OpenAI tier
GPT-5.3 Codex$1.75$14.00400kOptional priority tier can raise cost
DeepSeek V4$0.27$1.1064kPricing shown for deepseek-chat
Qwen 3.5 Plus$0.40$2.401MPricing varies by provider
MiniMax M2.5$0.15$1.20205kHigh-throughput analytical tasks
MiMo-V2-Flash$0.09$0.29262kUltra-low cost routine monitoring

DeepSeek V4: Cheap Tokens, Lots of Hype, Mixed Verification

DeepSeek V4, released at the time around China’s main annual “Two Sessions” parliamentary meetings in early March, is described as a 1-trillion parameter Mixture-of-Experts (MoE) architecture . It targets coding and repository-level reasoning with three specific technical improvements:

  • Engram conditional memory: A memory module designed to decouple static knowledge from reasoning, for fast retrieval at scale. Some reporting links this approach to million-token contexts (note the pricing in the above table is for 64k) and improved needle-in-haystack behavior, although those specific numbers and context limits are not independently confirmed in the public sources cited here.
  • Manifold-constrained hyper-connections (mHC): Reported training and architecture work intended to stabilize trillion-parameter scale models. Claimed benchmark uplifts are circulating, but should likely be treated as claims until a primary technical report is available.
  • Sparse attention (DSA): A reported efficiency approach intended to reduce compute overhead versus standard transformer attention, making very long context runs more economically viable.

On benchmarks, early reports claim 80%+ SWE-bench Verified performance in Opus territory while being dramatically cheaper. You might want to treat that as provisional until it shows up in a verifiable benchmark publication or an official DeepSeek technical report. Separately, there are credible reports that DeepSeek has optimized for domestic Chinese hardware, including Huawei Ascend, which may influence availability and pricing stability relative to Western GPU supply constraints.

GPT-5.3 Instant: Tone Calibration and Reliability

On 3 March 2026, OpenAI released GPT-5.3 Instant as a replacement for the 5.2 model. This update focused on “behavioral calibration” to remove the preachy tone and unnecessary refusals that inhibited previous versions.

Tone and directness matter in agent workflows because interaction quality affects execution. If your agent is browsing, writing, or handling routine back-and-forth, fewer unnecessary refusals and less padding make the model smoother to work with. A refusal at the wrong moment isn’t “safer,” it’s a stall.

Qwen 3.5: The Multimodal Agent Workhorse

Alibaba’s Qwen 3.5 is a native vision-language model designed for the “agentic AI era.” It utilizes a high-sparsity MoE architecture with 397 billion parameters, activating only 17 billion per token for speed .

  • Visual agency: Qwen 3.5 was trained jointly on text, images, and UI screenshots . It can navigate mobile and desktop interfaces, fill out forms, and ground its reasoning in pixel-level elements .
  • Benchmarks: It achieved a score of 78.6 in BrowseComp (agentic search), ranking second only to Claude Opus 4.6 . It also scored 90.8 on OmniDocBench v1.5, outperforming GPT-5.2 and Gemini 3 Pro in document recognition .
  • Efficiency: Qwen 3.5-Plus supports a 1-million-token context window by default, making it an ideal middle-tier model for search and document analysis tasks .

Qwen is pretty good and I recommend playing with it if you haven’t yet.

GPT-5.4: OpenAI’s Flagship Model Family

Since originally publishing this article in early March, OpenAI expanded its GPT-5.4 lineup. For OpenClaw users, the picture is now clearer: GPT-5.4 is the premium choice for complex reasoning and orchestration, GPT-5.4 Mini is the more practical day-to-day option for subagents, and GPT-5.4 Nano is the narrow utility tier for simple high-volume tasks.

Cost note: if you use OpenAI via ChatGPT/Codex OAuth in OpenClaw, you’re not using standard API billing. The real concern becomes token burn and plan limits, not runaway per-call API costs. Unlike OpenAI, Anthropic’s ToS forbids the use of Claude consumer-plan OAuth credentials in third-party tools. In practice, that means OpenClaw users cannot safely rely on Claude OAuth for external workflows and should expect to use API keys instead.

GPT-5.4 is OpenAI’s flagship model for agentic, coding, and professional workflows. It has a 1M context window starting at $2.50 per million input and $15.00 per million output, with much stronger performance on spreadsheets, documents, presentations, research, and multi-step tool use. It’s positioned by OpenAI as its most capable model for professional work.

GPT-5.4 Mini is another noteworthy addition for OpenClaw users. OpenAI describes it as its strongest mini model yet for coding, computer use, and subagents. It has a 400,000-token context window and costs $0.75 per million input tokens and $4.50 per million output tokens, which makes it a strong fit for fast, tool-heavy daily work and lower-cost subagent roles.

Finally, GPT-5.4 Nano is the cheapest 5.4-class model. It’s API-only and also has a 400,000-token context window, but OpenAI positions it for simple high-volume tasks such as classification, data extraction, ranking, and sub-agents. So it can be useful, but it’s not a suitable mainstream default for most OpenClaw setups.

Side note for anons: OpenClaw also supports Venice for privacy-focused inference, which I’ve wired and can recommend. If you’d like to try it out, use my link for 1,000 free tokens for new users. OpenClaw supports both fully private models and anonymized proxy access to major proprietary ones.

Operational Hardening in OpenClaw

OpenClaw releases on a near-daily basis at the moment, so it’s hard to keep up. Some recent infrastructure updates that support these new models include:

  1. External secrets management: A proper secrets workflow was added, allowing users to audit, configure, and reload API keys for multiple services (OpenRouter, DeepSeek, Slack) without restarting the gateway.   
  2. Reliable browser control: The Chrome extension connection has been improved to reduce stalls, and OpenClaw is moving toward smoother multi-agent browser driving. 
  3. Multi-agent routing: You can now bind separate agents (e.g., research vs. coding) to specific channels or accounts via the command line, which simplifies the management of complex setups .
  4. Messaging-first onboarding: New local installs now default to a messaging tools profile. Broad coding and system tools are no longer enabled by default to reduce security risks.
  5. Telegram streaming refinements: The update enables native draft-based streaming for private DMs, separating reasoning and answer lanes to provide a clearer preview of the model’s “thinking.”
  6. PDF and STT tools: A new first-class PDF analysis tool supports native Anthropic and Google backends. Additionally, a Speech-to-Text (STT) API allows audio file transcription through configured service providers.
  7. Plugin SDK update: The legacy registration handler was removed in favor of a new explicit HTTP route API, requiring users with custom plugins to update their integrations.
This is the video I wish I had had as a reference in January when I started toying with OpenClaw. Velvet Shark on YouTube does a thoroughly patient job of explaining memory, pruning, compaction and general good hygiene practices.


I don’t do fancy routing myself; I mainly use GPT-5.4 for coding and orchestration, and assign a specific model and fallback for each (sub) agent in all other task realms, then keep the scope tight so token burn stays relatively predictable. If you prefer automatic routing though, a simple tiered setup can also keep costs under control:

  • Tier 1: Strategic planning (GPT-5.4 or DeepSeek V4). Use GPT-5.4 for tasks requiring stronger reasoning, long context, polished deliverables, and final judgement. Use DeepSeek V4 when cost matters more and the workload leans toward broad multi-file analysis or refactoring.
  • Tier 2: Daily interactions (GPT-5.4 Mini or Qwen 3.5 Plus). GPT-5.4 Mini is now the more interesting OpenAI choice for day-to-day agent work. It’s much cheaper than full GPT-5.4, keeps a large 400K context window, and is explicitly positioned by OpenAI for coding, tool use, multimodal work, and subagents. Route visual tasks and web-heavy research to Qwen 3.5 Plus where it still performs well for the money.   
  • Tier 3: Routine monitoring (MiMo-V2-Flash or GPT-5.4 Nano). MiMo still makes sense for heartbeats and cheap monitoring. 5.4 Nano is the new OpenAI option when you deliberately want an API-only model for simple, high-volume support work such as classification, extraction, ranking, or lightweight code assistance. It is not the model I would choose as a general default.
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