Denne siden inkluderer både modellfamilier og en konkret modellkatalog med mange navngitte modeller som team aktivt evaluerer i 2026.
Why it is good: very strong general reasoning, coding quality, broad ecosystem, stable API operations.
Why it can be bad: higher cost in heavy-volume deployments, and less control than self-hosted alternatives.
Best for: production assistants, developer tools, high-quality chat and analysis workflows.
Why it is good: excellent long context use, strong writing quality, and careful style for policy-sensitive use.
Why it can be bad: may be more conservative than desired for some product experiences.
Best for: enterprise knowledge assistants, legal/ops writing, long-document review.
Why it is good: strong multimodal support, very capable for vision + text pipelines, good fit for Google Cloud users.
Why it can be bad: consistency can vary across prompt styles and niche coding tasks.
Best for: multimodal apps, search-enriched workflows, Google-native stacks.
Why it is good: open-weight flexibility, strong community support, and easier self-hosting options.
Why it can be bad: requires more in-house ML/platform work for top-tier quality and reliability.
Best for: cost-aware products, private deployments, custom fine-tuning paths.
Why it is good: efficient models with strong speed/quality balance and excellent European adoption.
Why it can be bad: ecosystem and tooling footprint can be narrower than hyperscaler platforms.
Best for: latency-sensitive assistants, compact model deployments, regional compliance cases.
Why they are good: often strong coding and reasoning performance for cost, popular in open model benchmarking.
Why they can be bad: deployment/compliance review is required in regulated enterprise environments.
Best for: benchmarking, private hosting experiments, value-focused inference layers.
Denne listen er bevisst bred, slik at du kan lage en kortliste med eksakte modellnavn før du kjører egne benchmarker.
Rask tilgang til de mest etterspurte modelsidene: GPT models, Claude models, Gemini models, Llama 3.1 8B, Llama 3.1 70B, Mixtral 8x7B, Qwen2.5 7B, Qwen2.5 32B, DeepSeek V3, DeepSeek R1, Phi-3 Medium.
| Model | Provider | Category | Good For | Watch Out | Download / Access |
|---|---|---|---|---|---|
| GPT-4.1 | OpenAI | Closed | General reasoning and coding | Premium cost | API access |
| GPT-4o | OpenAI | Closed multimodal | Fast assistant UX and multimodal tasks | Cost at high scale | API access |
| GPT-4o mini | OpenAI | Closed small | Cost-sensitive high-volume automation | Lower ceiling on hard reasoning | API access |
| o1 | OpenAI | Reasoning-first | Complex multi-step logic | Latency and cost per hard query | API access |
| o3-mini | OpenAI | Reasoning-efficient | Technical Q&A and coding workflows | Can require prompt tuning | API access |
| Claude 3.7 Sonnet | Anthropic | Closed | Long-context writing and analysis | Conservative tone in some flows | API access |
| Claude 3.5 Sonnet | Anthropic | Closed | Balanced quality and stability | Cost in very large traffic spikes | API access |
| Claude 3.5 Haiku | Anthropic | Closed small | Fast responses and triage | Less robust on deepest tasks | API access |
| Claude 3 Opus | Anthropic | Closed flagship | High-stakes synthesis | Throughput economics | API access |
| Gemini 2.0 Pro | Closed | Reasoning and multimodal enterprise apps | Task variance across prompt styles | API access | |
| Gemini 2.0 Flash | Closed fast | Low-latency assistant endpoints | Lower quality than premium tier | API access | |
| Gemini 1.5 Pro | Closed long-context | Very long document workflows | Price/performance depends on load | API access | |
| Gemini 1.5 Flash | Closed fast | Efficient summarization and extraction | Reasoning depth can be limited | API access | |
| Llama 3.1 405B Instruct | Meta | Open weight | Top-end open deployment quality | Heavy infrastructure requirements | Download · 70B · 8B |
| Llama 3.1 70B Instruct | Meta | Open weight | Strong self-hosted quality/cost balance | Needs good inference stack | Download · 405B · 8B |
| Llama 3.1 8B Instruct | Meta | Open weight small | Edge and low-cost deployments | Lower performance on complex tasks | Download · 70B · 405B |
| Llama 3.2 11B Vision | Meta | Open multimodal | Private vision-text pipelines | Requires evals for OCR-heavy cases | Download · 90B |
| Llama 3.2 90B Vision | Meta | Open multimodal | High-capacity multimodal inference | Infrastructure complexity | Download · 11B |
| Mistral Large | Mistral AI | Closed | High-quality enterprise assistants | Smaller ecosystem vs hyperscalers | API access |
| Mistral Medium | Mistral AI | Closed | Balanced production usage | Benchmark carefully vs peers | API access |
| Mistral Small | Mistral AI | Closed small | Fast cost-efficient chat | Limited depth on advanced reasoning | API access |
| Mixtral 8x22B | Mistral AI | Open MoE | Strong open-weight generation quality | Operational complexity | Download · 8x7B |
| Mixtral 8x7B | Mistral AI | Open MoE | Efficient self-hosting | Can trail latest closed models | Download · 8x22B |
| Codestral | Mistral AI | Code-specialized | Code generation and completion | Narrower general language strength | Download |
| Qwen2.5 72B Instruct | Alibaba | Open weight | Reasoning and multilingual tasks | Compliance checks for some regions | Download · 32B · 14B · 7B |
| Qwen2.5 32B Instruct | Alibaba | Open weight | Strong quality with lower infra cost | Prompt tuning often needed | Download · 72B · 14B · 7B |
| Qwen2.5 14B Instruct | Alibaba | Open weight | Balanced private deployment | Less robust on hardest tasks | Download · 72B · 32B · 7B |
| Qwen2.5 7B Instruct | Alibaba | Open weight small | High-throughput low-cost inference | Lower reasoning depth | Download · 14B · 32B · 72B |
| QwQ-32B | Alibaba | Reasoning open | Reasoning-focused private usage | Evals needed for stability | Download |
| DeepSeek V3 | DeepSeek | Open/available | General reasoning and coding value | Governance review in enterprise | Download |
| DeepSeek R1 | DeepSeek | Reasoning-focused | Difficult multi-step reasoning tasks | Latency on complex outputs | Download |
| DeepSeek Coder V2 | DeepSeek | Code-specialized | Developer assistants and code review | General writing less strong | Download |
| Command R+ | Cohere | Closed enterprise | RAG and enterprise knowledge use | Compare against top general models | API access |
| Command R | Cohere | Closed | Fast retrieval-grounded responses | Not always best for deep coding | API access |
| DBRX Instruct | Databricks | Open weight | Data platform integrated workloads | Requires platform maturity | Download |
| Phi-3 Medium | Microsoft | Small model | Compact deployments and edge use | Limited on very complex tasks | Download · Mini |
| Phi-3 Mini | Microsoft | Small model | On-device and constrained inference | Lower accuracy ceiling | Download · Medium |
| Yi-34B Chat | 01.AI | Open weight | Multilingual experimentation | Needs thorough evaluation before prod | Download |
Note: closed models usually provide API access rather than direct weight downloads.
Do not pick only by benchmark rank. Validate with your own workload: prompt complexity, response latency, failure tolerance, and monthly token budget.
Continue with the comparison matrix and then read clear recommendations.