Choosing the right AI model to power your Autohive agent

When building an agent in Autohive, you pick which AI model runs it. That choice matters. Different models are better at different things, and picking the wrong one for the job means slower results, higher costs, or outputs that miss the mark.

OpenAI, Anthropic, Google, and xAI have all shipped major updates in 2025 and 2026. There are also strong open-source options now available through Fireworks. This guide cuts through the noise and tells you what each model is actually good at.

The providers

OpenAI

OpenAI brought large language models to the mainstream and remains one of the most widely used providers. Their models tend to be reliable and consistent, with strong instruction-following and broad integration support. You’ve probably used them through ChatGPT or Microsoft Copilot.

Anthropic

Anthropic was founded by former OpenAI researchers and focuses on building AI that behaves predictably and safely. Their Claude models use constitutional AI, a training approach where the model follows a set of guiding principles rather than just responding to prompts. Claude models are well regarded for analytical work, long-context tasks, and compliance-sensitive environments. Anthropic also developed the Model Context Protocol (MCP), which is becoming a standard for how agents connect to tools.

Gemini

Google’s Gemini models are developed by DeepMind and baked into products like Gmail, Docs, and Android. They combine strong language understanding with Google’s knowledge and reasoning capabilities. Gemini models are particularly good with multimodal inputs and high-volume, document-heavy workflows.

xAI

Founded by Elon Musk in 2023, xAI has grown quickly into a serious AI lab. Their Grok models are built with less content filtering than most providers, which makes them useful for candid, unfiltered outputs. Grok also pulls real-time data from X (formerly Twitter), giving it current context that other models don’t have.

Open-source models via Fireworks

Autohive also supports a selection of open-source models served through Fireworks, a high-performance AI inference platform. These models come from different labs and offer a compelling alternative to closed-source providers, particularly for teams that want strong performance at lower cost. Because they’re open-weight, they tend to be more transparent and easier to inspect than proprietary options.

All open-source models in Autohive run through AWS. Your data never goes directly to the source labs. Requests are routed through a secure AWS environment, so the same data security standards apply here as they do across the rest of the platform.

Current models

OpenAI (GPT-5.6 Sol, GPT-5.6 Terra, GPT-5.6 Luna)

OpenAI shipped the GPT-5.6 family on July 9, 2026, superseding the GPT-5.5 and GPT-5.4 line. It comes in three tiers, all sharing a 1M token context window and up to 128K output tokens per response, so the choice between them is about capability and cost rather than how much they can read.

GPT-5.6 Sol (Released July 9, 2026) OpenAI’s flagship, built for complex reasoning, coding, and long-horizon agentic work. It tops Artificial Analysis’s Coding Agent Index with a score of 80 and is noticeably more token-efficient than GPT-5.5 on coding tasks. Use it for the hardest problems you’d hand to an OpenAI model.

GPT-5.6 Terra (Released July 9, 2026) The balanced everyday option. OpenAI positions it as matching GPT-5.5 performance at roughly half the cost, which makes it the sensible default for general-purpose agents.

GPT-5.6 Luna (Released July 9, 2026) The fastest and most affordable model in the family. Good for classification, triage, summarization, and anything high-volume where per-call cost adds up.

Pros:

• Sol leads the field on agentic coding benchmarks
• Terra delivers GPT-5.5-level capability at a lower price point
• Luna is a strong cost-performance option for agents that run at scale
• 1M token context window across all three tiers
• Consistent, structured output with minimal surprises
• Wide ecosystem support and integrations

Cons:

• Independent testing suggests Sol trails the strongest Claude models on some complex coding tasks, so it’s worth testing both on your own workload
• Can be overly cautious with vague prompts
• Proprietary, so customization is limited

Best for:

General-purpose agents that need reliable, structured output. Email drafting, ticket triage, follow-up automation, coding tasks. Use Luna for high-volume work, Terra for everyday agents, and Sol for the hardest reasoning and coding problems.

Anthropic (Claude Opus 5, Claude Sonnet 5, Claude Haiku 4.5)

Claude Opus 5 (Released July 24, 2026) Anthropic’s new flagship, replacing Opus 4.8. Major gains on long-horizon agentic work, business process automation, computer use, and scientific research, with a 1M token context window and up to 128K output tokens per response. See Anthropic’s announcement for the full release notes.

Claude Sonnet 5 (Released June 30, 2026) The default Anthropic model in Autohive. The most capable Sonnet yet, built as a more affordable path to agentic AI. It narrows the performance gap with Opus on agentic benchmarks while keeping costs lower. A good starting point for most teams.

Claude Haiku 4.5 The fastest and most cost-effective Claude model. It handles high-volume, lightweight tasks well: triage, classification, quick summaries, simple Q&A.

Pros:

• Opus 5 leads on business workflow automation, computer use, and long-horizon agentic tasks
• Sonnet 5 brings near-Opus agentic capability at a lower price point
• Haiku 4.5 is fast and cost-effective for high-volume workflows
• Stable and coherent over long tasks and large inputs
• Strong safety and bias controls for compliance-sensitive work

Cons:

• Opus 5 is slower and more expensive than Sonnet. Only use it when you need it.
• Vague prompts can produce overly cautious responses

Best for:

Analytical tasks, multi-step agentic workflows, structured output, compliance-heavy environments. Use Haiku 4.5 for speed and cost, Sonnet 5 for capable everyday agents, Opus 5 for the hardest problems.

Google Gemini (Gemini 3.6 Flash, Gemini 3.1 Pro, Gemini 3.5 Flash-Lite)

Gemini 3.6 Flash (Released July 23, 2026) Google’s latest fast-tier model, and the strongest in the Flash line so far. It handles coding assistance, document analysis, multimodal tasks, and production workloads well below frontier pricing. That makes it the default choice when your agent needs more than basic text work but not frontier-level reasoning. Read more in our Gemini Flash models announcement.

Gemini 3.1 Pro (Released March 2026) Google’s reasoning flagship: massive context window, strong multimodal capability, and solid benchmark scores on reasoning tasks. Best when depth and accuracy matter more than speed.

Gemini 3.5 Flash-Lite (Released July 23, 2026) The fastest, cheapest model in the Gemini range. Built for high-volume, low-latency work: classification, extraction, summarization, and background jobs. A strong pick for worker or subagent roles in multi-agent workflows, and for scheduled jobs where per-call cost adds up.

Pros:

• Gemini 3.6 Flash handles agentic and coding tasks at production speed, below frontier pricing
• Gemini 3.1 Pro covers massive context windows for document-heavy work
• Gemini 3.5 Flash-Lite is one of the most affordable capable models available
• Multimodal support across text, images, audio, and video
• Tight integration with Google Cloud

Cons:

• Tone can be formal, less suited for open-ended or creative tasks
• Less flexibility in conversational style compared to Claude or GPT
• Flash-tier models trade some reasoning ceiling for speed, so high-stakes tasks still call for Gemini 3.1 Pro or another frontier model

Best for:

High-throughput agents working with long documents, CRM enrichment, knowledge base summarization, and data tagging. Use Flash-Lite for worker roles in multi-agent setups and for scheduled or recurring jobs.

xAI Grok (Grok 4.3, Grok 4.20)

Grok 4.3 (Released April 2026) xAI’s recommended model for production use. 1M token context window, improved architecture over Grok 4.20, and a December 2025 knowledge cutoff. Good balance of accuracy and speed for demanding tasks.

Grok 4.20 (Released early 2026) Available in standard and reasoning variants. Introduced xAI’s reinforcement-learning-refined reasoning approach. Still widely used, particularly for tasks that benefit from extended reasoning.

Pros:

• Grok 4.3 is xAI’s current recommendation for production reasoning
• Real-time X data gives it current context other models lack
• Less-filtered outputs are useful for honest brainstorming and internal content
• Both models support 1M token context windows

Cons:

• Unpredictable when tight control over tone or formatting is needed
• Not suitable for customer-facing or compliance-heavy environments
• Real-time data is specific to X, which doesn’t cover every domain
• Fewer ecosystem integrations than OpenAI or Google

Best for:

Internal agents focused on ideation, feedback, team brainstorming, and anything where real-time X data or a candid tone is more useful than filtered output.

Open-source via Fireworks (DeepSeek V4 Pro, Kimi K2.6, GLM 5.2, Qwen 3.7 Plus)

These models come from different AI labs and are served through Fireworks. They’re open-weight, meaning the model weights are publicly available. On key benchmarks, the best of these now compete directly with top closed-source models.

All open-source models in Autohive run through AWS. Your data never goes directly to the source labs. Requests are routed through a secure AWS environment, so the same data security standards apply here as they do across the rest of the platform.

DeepSeek V4 Pro (Released April 24, 2026, by DeepSeek) The top open-weight model on several coding benchmarks, including an 81% SWE-bench score. Built on a 1.6T parameter mixture-of-experts architecture with a 1M context window. NIST’s CAISI evaluation confirmed it competes with leading US frontier models across most benchmarks. A strong pick when coding performance at lower cost is the priority.

Kimi K2.6 (By Moonshot AI) 1T total parameters (32B active), with a focus on agentic and coding tasks. Benchmarks place it alongside Sonnet 4.6 and GPT-5.4 on coding evaluations. Solid choice for teams wanting capable open-source performance with broad task coverage.

GLM 5.2 (By Zhipu AI) Consistently ranks near the top of open-source coding benchmarks. Active development from Zhipu AI keeps it current. A reliable open-weight option with strong coding and reasoning performance.

Qwen 3.7 Plus (By Alibaba) Part of Alibaba’s Qwen 3 family. Strong across coding, reasoning, and general-purpose tasks. Broad community support and active development make it one of the more dependable open-source choices available right now.

Pros:

• Open-weight models are more transparent and inspectable than proprietary alternatives
• DeepSeek V4 Pro delivers frontier coding performance at competitive cost
• All four models are actively developed and well regarded in the open-source community
• Competitive with top closed-source models on many benchmarks

Cons:

• Less integrated ecosystem support compared to the major closed-source providers
• Safety filtering and behaviour varies across labs
• Better suited for teams comfortable evaluating and testing open-source models

Best for:

Teams that want strong coding and reasoning capability with more transparency, or those looking to diversify away from a single closed-source provider.

Quick reference

As you build more agents, you’ll likely find that different models work better for different jobs. Autohive lets you switch models, test performance, and adjust as you go. The goal isn’t finding the single best model. It’s finding the right one for what you’re building right now.

We’ll keep updating this guide as new models ship. If there’s a model or provider you’d like to see supported, let us know.

Build your own AI agents on Autohive, the no-code AI platform.

Previous models on Autohive

These models were featured in earlier versions of this guide. They’ve been replaced by newer releases from the same providers, but you may still encounter them referenced in older agent configurations.

OpenAI: GPT-5.5, GPT-5.4, GPT-5.4 Mini, GPT-5, GPT-4.1, o4-mini, o3

Anthropic: Claude Opus 4.8, Claude Opus 4.1, Claude Sonnet 4.5, Claude 3.7 Sonnet

Google: Gemini 2.5 Pro, Gemini 2.5 Flash, Gemini 3.5 Flash, Gemini 3.1 Flash-Lite

xAI: Grok 4, Grok 3, Grok 3 Mini