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Current AI Models Compared: GPT, Claude and Gemini versus DeepSeek and Qwen

Current AI Models Compared: GPT, Claude and Gemini versus DeepSeek and Qwen
Contents
  1. The concrete comparison set
  2. Access and operational control
  3. Ollama is the execution layer
  4. A useful shortlist
  5. Related tools
  6. Questions and answers
  7. Sources

A comparison of current AI models needs more than a leaderboard. A chat product, a model API and downloadable weights offer different forms of access. This overview is dated 6 October 2026 and uses official documentation. No original performance measurements underpin the assessment.

Four checks for model selection: Task; Model + version; Data path; Quality test; Operation + cost
An evaluation method rather than a benchmark ranking.

The concrete comparison set

OpenAI lists GPT-6 Astra, GPT-6.1 Sol and GPT-6 Luna. Anthropic lists Claude Fable 5.1, Opus 5.5 and Sonnet 5.5. Google documents Gemini 3.8 Flash as stable. DeepSeek V4.1 Flash and Qwen3.8-27B provide concrete references on the open-weight side. A documented API does not establish access to every model in every consumer subscription. [1, 3, 6, 7, 8]

Access and operational control

Family Concrete candidates Deployment Key check
GPT Astra / 6.1 Sol / Luna Hosted API Quality target and effort
Claude Fable 5.1 / Opus 5.5 / Sonnet 5.5 Hosted API Agent workflow and tools
Gemini 3.8 Flash Hosted API Documents and input modalities
DeepSeek V4.1 Flash API or published weights Full model and memory
Qwen 3.8-27B Weights / suitable serving service Artifact, runtime and quantization

DeepSeek V4.1 Flash weights use the MIT licence; Qwen3.8-27B uses Apache 2.0. These specific licence statements do not establish the terms of other variants. Open-weight access does not mean that all training data is public or that operation is free. [7, 8]

Ollama is the execution layer

Ollama is not another foundation model. It manages and runs models and exposes an API. A meaningful comparison therefore pairs GPT-6.1 Sol with an exact Qwen or DeepSeek checkpoint running under a recorded Ollama version. The model tag, quantization and context belong in the test log. Ollama also supports cloud features, so its name alone does not establish a local data path. [9]

A useful shortlist

Astra and Opus are reasonable candidates for demanding cloud tasks; Sol and Sonnet extend the comparison when budgets are tighter. Gemini warrants evaluation for mixed inputs. This is an editorial shortlist based on documented capabilities, rather than measured superiority. For local deployment, a smaller Qwen checkpoint is generally a more practical starting point than a complete large DeepSeek model.

A pilot uses the same tasks, explicit acceptance criteria and repeated runs. Errors, editing effort, elapsed time and cost are recorded separately. The result is a suitable combination of quality, data path and operating effort rather than a supposedly universal winning brand.

Questions and answers

Is Ollama a competitor to GPT or Claude?

Ollama is a runtime and model manager. Concrete models and deployment conditions are comparable; the software alone has no independent model quality.

Are open-weight models automatically suitable for local use?

Downloadable weights provide one prerequisite. Architecture support, memory, quantization and licensing still require separate checks.

Sources

  1. OpenAI: model catalogue
  2. OpenAI: GPT-6.1 Sol
  3. Anthropic: Claude model overview
  4. Anthropic: Claude Opus 5.5
  5. Anthropic: Claude Sonnet 5.5
  6. Google: Gemini 3.8 Flash
  7. DeepSeek: V4.1 Flash model card
  8. Qwen: Qwen3.8-27B model card
  9. Ollama: FAQ and local/cloud operation
  10. OpenAI: API pricing
  11. DeepSeek: models and pricing
  12. Google: document understanding

Sources checked: 6 October 2026.

Lukas Wojcik

Lukas Wojcik

Systems architect and technology enthusiast specializing in scalable tracking solutions, GMP Stack (GA4 & GTM), and robust backend architectures. Advocate for clean code and privacy-first design.

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