Global Araç
Frontier Model Tracker
| Model | Sağlayıcı | Çıkış | Bağlam | Giriş | Çıkış | Öne çıkanlar |
|---|---|---|---|---|---|---|
| Claude Opus 4.7 | Anthropic | 2026-04 | 1M | $15.00 | $75.00 | 1M context · Best at agentic SWE · Strong reasoning |
| Claude Sonnet 4.6 | Anthropic | 2026-02 | 1M | $3.00 | $15.00 | 1M context · Default daily driver · Tool use |
| Gemini 3 Pro | 2025-12 | 2M | $2.50 | $10.00 | 2M context · Native multimodal | |
| Claude Haiku 4.5 | Anthropic | 2025-10 | 200k | $0.80 | $4.00 | Fastest Claude · Budget agentic |
| DeepSeek V3.2 | DeepSeek | 2025-09 | 128k | $0.27 | $1.10 | Cheapest frontier · Open weights |
| Qwen 3.5 72B | Alibaba | 2025-09 | 128k | open | open | Open weights · Top SWE-bench OSS |
| GPT-5 | OpenAI | 2025-08 | 400k | $2.50 | $10.00 | Reasoning router · Vision native |
| GPT-5 mini | OpenAI | 2025-08 | 400k | $0.25 | $2.00 | Cheap reasoning · Tool use |
| Grok 4 | xAI | 2025-07 | 256k | $3.00 | $15.00 | Real-time data · X integration |
| Gemini 2.5 Pro | 2025-06 | 2M | $1.25 | $5.00 | 2M context · Audio + video | |
| Mistral Large 3 | Mistral | 2025-05 | 128k | $2.00 | $6.00 | EU hosting · Tool use |
| Kimi K2 | Moonshot | 2025-04 | 1M | $0.60 | $2.50 | 1M context · Open weights |
| Llama 4 Maverick | Meta | 2025-04 | 1M | open | open | Open weights · MoE |
| DeepSeek R1 | DeepSeek | 2025-01 | 128k | $0.55 | $2.19 | Open weights · Reasoning |
| Llama 3.3 70B | Meta | 2024-12 | 128k | open | open | Open weights · Self-host |
The frontier-model landscape in 2025-2026 has stratified into three tiers: closed frontier (Anthropic Claude family, OpenAI GPT-5 family, Google Gemini family — Top quality, premium pricing, restricted access), open-source frontier (Meta Llama 3.3/4, DeepSeek V3.2/R1, Qwen 3.5, Kimi K2 — comparable quality to closed, free or self-hosted, geopolitically diverse providers), and specialty (Grok 4 for x.com integration, Mistral Large 3 for EU data residency, smaller specialized models for vertical use cases). The space moves fast — significant new releases roughly every 2-3 months, with capability rankings shuffling on each iteration. A January model recommendation is often outdated by April. Active monitoring matters for builders making infrastructure decisions.
The tracker covers ~15 most-relevant frontier models with key fields: release date, provider, parameter count where known, context window, vision/audio/video input modality, key benchmarks (MMLU, GPQA, HumanEval, MATH, agent benchmarks like SWE-bench), pricing (input/output per 1M tokens), recommended use case (code / reasoning / vision / long-context / agents). Filter by capability dimension or sort by release date for quick scanning. Useful for: builders choosing which model to integrate, teams comparing model capability for specific tasks, researchers tracking the field, and decision-makers justifying which provider to standardize on.
Practical infrastructure considerations this surfaces: (1) Lock-in vs flexibility — closed-frontier models have proprietary features (Anthropic computer use, OpenAI file search, Gemini tools) that don't port. Open-source models are commodity- like, easy to switch. (2) Cost vs quality — DeepSeek V3.2 at $0.27/1M input tokens is 10× cheaper than Claude Sonnet at $3/1M input, but quality gap matters for some tasks (less for routine, more for hard reasoning). (3) Geopolitical considerations — DeepSeek and Qwen are Chinese-trained; Mistral is French; Llama is American. Choose based on data residency requirements and corporate compliance policies. (4) Speed vs quality — Haiku / Flash / mini / DeepSeek V3 prioritize speed; full Claude Sonnet / GPT-5 / Gemini Pro prioritize quality. Most production use cases can route appropriately. (5) Reasoning vs general — reasoning models (Claude with extended thinking, OpenAI o3, Gemini deep-thinking) are 5-10× more expensive but dramatically better for math, code, complex reasoning. Don't use them for chat / classification.
Nasıl Kullanılır
- Pick a capability filter (code, reasoning, vision, long context, agents).
- Read released models sorted newest-first.
- Compare benchmark scores, pricing, and context window.
- Identify the best fit for your specific task.
- Re-check periodically — frontier rankings shift every 2-3 months.
Ne Zaman Kullanılır
- Choosing which LLM to integrate for a new product.
- Quarterly model evaluation — should you switch from your current model to a new release?
- Comparing closed-frontier vs open-source for cost/quality tradeoffs.
- Investor pitch decks needing current state-of-the-art context.
- Researchers tracking the field for academic or strategic purposes.
Ne Zaman Kullanılmaz
- Specific niche specializations (medical AI, legal AI, scientific research models) — those have separate vertical-specific landscapes.
- Edge / on-device models (Phi, Gemma small, MobileLLM) — different category for different use cases.
- Code-completion-only tools (Codeium, Cursor's underlying models) — those are productized differently.
- Image / video / audio generation models — separate landscape from text models.
Yaygın Kullanım Senaryoları
- Pre-decision sanity-check on inputs and outputs
- Educational use — demonstrating the underlying concept
- Onboarding a colleague who needs the same calculation/conversion
- Verifying a number or output before passing it on
Sık Sorulan Sorular
What's a ‘frontier model’?
Loosely defined — the leading-edge LLMs that are competitive on top public benchmarks (MMLU, GPQA, HumanEval, SWE-bench). Currently dominated by Anthropic Claude family, OpenAI GPT-5 family, Google Gemini family, with strong open-source contenders from DeepSeek, Meta, Qwen, Mistral. The line shifts as new releases push the frontier; some “frontier” models from 2023 are now mid-tier in 2025.
Closed vs open-source — which should I use?
Closed (Anthropic, OpenAI, Google): top quality, premium pricing, restricted access, proprietary features that don't port. Open-source (DeepSeek, Llama, Qwen, Mistral): comparable quality at top end, much cheaper or self-hostable, easier to switch providers. For high-volume routine tasks: open-source wins on cost. For hard tasks needing best quality: closed often still wins. Hybrid (open-source for routine, closed for hard) is increasingly common.
How often do frontier models update?
Significant new releases every 2-3 months from major labs. Anthropic Claude family: roughly quarterly major versions. OpenAI: similar cadence with GPT-5 releases. Google Gemini: monthly minor updates, quarterly major. DeepSeek and Chinese labs: aggressive 6-8 week cadence. Open-source: continuous community fine-tunes. The rapid pace means “current best” recommendations are stale within months; check trackers like this one regularly.
What are reasoning models?
Models that produce chain-of-thought reasoning before final answer (Anthropic Claude with extended thinking, OpenAI o1/o3 family, Gemini deep-thinking). 5-10× more expensive than non-reasoning models but dramatically better at math, code, complex multi-step problems. Don't use for simple tasks (chat, classification, summarization) where overhead doesn't pay off. Use for: hard math, debugging code, multi-step planning, careful analysis.
Are Chinese models safe to use?
Depends on your context. DeepSeek and Qwen are excellent open-source models — accessible via Hugging Face, can be self-hosted entirely on your infrastructure (no data goes to China). API access via DeepSeek's servers does send data to China; corporate policy may prohibit. Most enterprises avoid sending sensitive data to any non-US-hosted API; same applies to Chinese providers. For self-hosted use, the models are well-vetted and safe.
How do I keep up?
Recommended sources: TheVerge AI, Anthropic / OpenAI / Google blogs (provider-direct), Andrej Karpathy / Sam Altman / Dario Amodei tweets for landscape commentary, Hacker News for community reaction, lmsys leaderboard (chatbot arena) for blind preference testing, livebench.ai for fresh benchmarks. Beware benchmark-only takes — qualitative differences in real use often diverge from benchmark scores.