Core References

AI Model Reference Guide

Compare current AI model families, understand reasoning and sampling controls, and choose models for research reasoning, writing, and analysis tasks

The available models change fast; the task-fit question stays constant.

Overview: current, but not permanent

Warning

AI model names, aliases, pricing, rate limits, and control parameters change quickly. We spot-checked this page against provider documentation on July 27, 2026. Before building a workflow around a specific model ID, check the provider's current model list.

The practical question is not "which model is best?" It is which model, access path, and control surface fit the task:

Reasoning depth: Can it handle theoretical tension and multi-step analysis?
Context capacity: Can it read the relevant corpus without destructive chunking?
Writing behavior: Does it preserve academic nuance and voice?
Cost and latency: Can you afford to iterate?
Control surface: Does it want reasoning effort, thinking level, prompt constraints, or sampling parameters?

Which AI Model Fits This Task?

Research Memex currently separates worker choice from coordination choice.

Good starting points:

Claude Opus 5 or Claude Sonnet 5 for theory, critique, nuanced writing, and focused implementation.
GPT-5.6 Sol or Kimi K3 when the same model may also need to coordinate later.
Gemini 3.6 Flash or Gemini 3.1 Pro preview for large-context synthesis.
GLM-5.2 for long-context coding-agent reasoning and Chinese-English synthesis.

Use cases: theory building, critical analysis, synthesis, methodology design, and one scoped coding task.

Working rule: start with cheaper or free access for exploration, then move the strongest prompt and evidence set to the model you trust for final reasoning.

See the AI Model Reference Guide for the full comparison.

Current model families

OpenAI GPT Family

Current provider-native IDs: gpt-5.6-sol, gpt-5.6-terra, and gpt-5.6-luna. The gpt-5.6 alias routes to Sol.
Context: roughly 1M on Sol and Terra; 400K on Luna.
Control surface: reasoning.effort where supported; sampling defaults are usually enough unless a specific API task needs tuning.
Best for: Sol for difficult reasoning, implementation, and coordination; Terra for balanced work; Luna for high-volume work.
Source: OpenAI latest-model guide

Anthropic Claude Family

Current provider-native IDs: claude-fable-5, claude-opus-5, and claude-sonnet-5; Claude Haiku 4.5 remains the faster, lighter option.
Context: 1M on Fable 5, Opus 5, and Sonnet 5.
Control surface: use effort and thinking controls where documented. Fable 5 always uses adaptive thinking; keep sampling defaults unless the current model page says otherwise.
Best for: Fable 5 for long-running agent coordination; Opus 5 and Sonnet 5 for bounded analysis, writing, and implementation tasks.
Source: Claude model docs

Kimi Family (Moonshot AI)

Current provider-native ID: kimi-k3. Kimi K2.7 Code and K2.7 Code Highspeed remain coding specialists; Kimi K2.6 remains a lower-cost general option.
Context: 1M on Kimi K3; 256K on the current K2 models.
Control surface: Kimi K3 always thinks and accepts reasoning_effort values low, high, and max; the default is max. Omit sampling parameters.
Best for: Kimi K3 as either a direct worker or a multi-model coordinator; K2.7 Code for focused coding and review.
Source: Kimi K3 quickstart

Google Gemini Family

Current anchor: Gemini 3.6 Flash and Gemini 3.5 Flash-Lite stable; Gemini 3.1 Pro remains a preview option.
Context: Gemini handles large contexts well; exact limits and rate limits vary by tier and model.
Control surface: Gemini 3.6 Flash, Gemini 3.5 Flash-Lite, and future releases deprecate temperature, top_p, and top_k; use thinking_level where relevant.
Best for: large-corpus synthesis, volume processing, multimodal tasks, Google platform workflows.
Source: Gemini latest-model guide

DeepSeek Family

Current anchor: deepseek-v4-pro and deepseek-v4-flash.
Migration note: use the V4 model IDs directly. This guide no longer carries the retired compatibility aliases.
Control surface: current V4 models use thinking mode plus reasoning_effort; the default thinking mode is enabled.
Best for: high-volume exploration, budget-sensitive screening, reasoning checks where cost matters.
Source: DeepSeek pricing/model docs

GLM Family (Z.ai / Zhipu AI)

Current anchor: GLM-5.2.
Context/output: 1M context and up to 128K output.
Control surface: GLM-5.2 supports reasoning_effort; default temperature is 1.0 if you set sampling at all.
Best for: long-context coding-adjacent reasoning, multilingual Chinese-English synthesis, frontier-adjacent open-weight experiments.
Source: GLM-5.2 docs

Qwen Family (Alibaba)

Current anchor: Qwen3.7-Max and Qwen3.7-Plus, with Qwen3.6-Flash as the lower-cost current Flash option.
Context: current Max and Plus entries support up to 1M in the documented deployment modes.
Lineup note: Max is the difficult-task model; Plus balances capability, speed, and cost; Flash is the volume option.
Caveat: model names and availability differ by international, global, US, EU, Hong Kong, and Chinese mainland deployment modes.
Best for: multilingual work, open-source comparisons, region-specific provider setups.
Source: Alibaba Model Studio model list

xAI Grok Family

Current anchor: Grok 4.5 for general and coding work.
Context: Grok 4.5 lists a 500K-token context window.
Control surface: reasoning_effort supports low, medium, and high; the default is high.
Best for: agentic tool calling, coding, broad general tasks, xAI platform experiments.
Source: xAI model docs

Local / Open-Weight Models

Current anchor: depends on your runtime, hardware, and privacy constraints.
Best for: sensitive data, reproducibility, offline work, customization.
Caveat: local quality depends more on deployment details than provider marketing pages.

Access paths

Access path	Best for	Notes
Provider web app	Fast exploration	Good for trying a model before wiring API access.
Google AI Studio	Gemini API keys and free-tier experiments	Use for direct Gemini model access, not Antigravity CLI auth.
Cherry Studio	GUI model comparison and knowledge bases	Best first interface for many researchers.
OpenCode	CLI-side provider comparison	Useful when model choice is part of the method.
Vox MCP	Multi-model access inside an MCP client	Pure passthrough; good for challenge and triangulation.
Claude Code / Antigravity CLI	Agentic project work	Product sign-in flows, not generic provider-key clients.

Configuration: defaults first

Sampling is no longer the default lever

The old advice was to tune temperature for every model. That is now misleading. Many current reasoning models either reject sampling changes, ignore them, or explicitly recommend defaults.

Default rule: omit temperature, top_p, and top_k unless the provider currently documents that the model supports and benefits from changing them.

Provider-specific notes:

Provider/model	Recommendation
Claude 5 family	Keep sampling defaults; use `effort` and the model's documented thinking controls.
Gemini 3.6 Flash / 3.5 Flash-Lite	Remove deprecated `temperature`, `top_p`, and `top_k`; use `thinking_level` and prompt constraints.
Kimi K3 / K2.7 / K2.6	Omit sampling parameters; use `reasoning_effort` on K3.
GLM-5.2	Default temperature is `1.0`; prefer `reasoning_effort` for thinking control.
DeepSeek V4	Use thinking mode and `reasoning_effort`; keep other controls at provider defaults.
xAI Grok 4.5	Prefer `reasoning_effort`; watch incompatible parameters on reasoning models.

If a compatible model requires a temperature and the provider offers no task-specific guidance, start at 1.0. Go lower only for mechanical extraction or classification where repeatability matters.

Prefer reasoning controls when available

Control	Providers/examples	Use for
`reasoning.effort` / `reasoning_effort` / `effort`	OpenAI, Claude, Kimi K3, xAI, GLM, DeepSeek	More or less deliberate reasoning.
`thinking_level`	Current Gemini models	Gemini reasoning depth without raw sampling changes.
thinking mode	Kimi K2.6, DeepSeek V4, GLM	Choosing thinking behavior where supported.
Sequential Thinking MCP	Any MCP-capable client	Tool-visible, revisable reasoning steps.

Use high reasoning effort for theoretical integration, methods design, and hard debugging. Use low/default effort for routine extraction, tagging, and quick checks.

Task type	Preferred control	Suggested starting point
Theory synthesis	Reasoning effort / thinking level	Medium or high; keep sampling defaults.
Framework building	Reasoning effort plus explicit criteria	High; ask for alternatives and tensions.
Literature synthesis	Context capacity plus structured prompt	Medium/high thinking; defaults for sampling.
Draft writing	Prompt constraints and examples	Defaults first; ask for voice and structure directly.
Citation extraction	Deterministic prompt rules	Low reasoning or provider-specific low temp if supported.
Systematic coding	Rubric and validation pass	Low/medium reasoning; prioritize consistency checks.

Strategic model usage

Use cheap or free access paths to learn model behavior against your own corpus.

Start with Google AI Studio, Cherry Studio, OpenCode, or OpenRouter.
Run the same prompt across at least three model families.
Record failure modes, not just best answers.
Promote a model only after it handles your real material.

Work shape	Good starting models	Why
One bounded theory, critique, or writing task	Claude Opus 5 or Claude Sonnet 5	Worker-first choices for a single chat task.
Multi-model coordination	Claude Fable 5, GPT-5.6 Sol, or Kimi K3	Better starting points when the session must direct several models.
Alternating between coordination and direct work	GPT-5.6 Sol or Kimi K3	Both are strong coordinators and direct workers.
Large literature sets	Gemini 3.6 Flash, Gemini 3.1 Pro preview, GLM-5.2	Large context and long-horizon reasoning.
Budget-conscious screening	DeepSeek V4, Gemini Flash/Lite, Qwen Plus/Flash	Cheap enough to iterate.

Free and low-cost options

Google AI Studio remains the easiest free/low-cost backup for Gemini API work, but limits change often. Check Gemini rate limits before relying on a quota for teaching or batch work.

Other cost-management paths:

Use OpenCode or Cherry Studio to compare providers before committing to a long run.
Use DeepSeek or Qwen for high-volume screening when quality is sufficient.
Use local models when privacy matters more than frontier quality.
Reserve expensive models for the final synthesis, argument, or validation pass.

Next step

The reference above tells you what is available. The companion page tells you how to test those choices against your own materials: AI Model Discovery Protocol.

cite this page

Lin, X. (2026). AI Model Reference Guide. Research Memex. https://research-memex.org/docs/implementation/core-references/ai-model-reference-guide

@misc{docs-implementation-core-references-ai-model-reference-guide-2026,
  author = {Xule Lin},
  title = {AI Model Reference Guide},
  year = {2026},
  howpublished = {\url{https://research-memex.org/docs/implementation/core-references/ai-model-reference-guide}},
  note = {ORCID: 0000-0001-7885-4194}
}

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