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LLM Analysis Which One Is The Best?

· 4 min read
Ilham Surya
Ilham Surya
SRE Engineer - Fullstack Enthusiast - Go, Python, React, Typescript

This post refreshes the LLM landscape as of Dec 2025. Quick takes on who’s best at what, how to choose, and which open-weight models are worth self-hosting.

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How to choose (2025 checklist)​

  • Quality vs reasoning: reasoning-heavy tasks (planning, code review) benefit from o1/GPT-4.1 or Claude 3.5 Sonnet; light Q&A can use Gemini 2.0 Flash or mid-tier open weights.
  • Latency: Gemini 2.0 Flash and Mistral Small are the quickest; o1 and Sonnet reason slower but more deeply.
  • Context window: All major frontier models handle ≥200K tokens; for huge docs (>1M) use chunk+RAG instead of raw context dumping.
  • Cost: Tokens still rule. Use cheap inference (Flash/Haiku/Mini) for bulk; reserve premium models for final passes.
  • Modalities: If you need image/code interpreter tools, check the model’s native toolset (GPT-4.1, Gemini 2.0 are most complete).
  • Data control: Enterprise/regional data residency may steer you to Anthropic (EU), Vertex/GCP, or self-hosted open weights.

Quick picks (my 2025 snapshot)​

  • Reasoning & coding: OpenAI o1 / GPT-4.1, Anthropic Claude 3.5 Sonnet.
  • Fast & cheap: Gemini 2.0 Flash, Claude 3.5 Haiku, Mistral Small, Qwen2.5 7B/14B.
  • Balanced generalist: Claude 3.5 Sonnet, Gemini 2.0 Pro, GPT-4.1 Mini when cost/latency matter.
  • Open-weight to self-host: Llama 3.3 70B Instruct, Qwen2.5 72B, Mistral Large 2; smaller: Llama 3.3 8B, Gemma 2 9B.

Providers (frontier)​

OpenAI​

  • Models: GPT-4.1, GPT-4.1 Mini, o1 (reasoning-first).
  • Strengths: top-tier reasoning, code, tools; ecosystem maturity.
  • Watchouts: pricing for long contexts; regional data residency is limited.

Anthropic (Claude)​

  • Models: Claude 3.5 Sonnet, Claude 3.5 Haiku.
  • Strengths: balanced quality/cost, strong writing+analysis, generous context, good safety defaults.
  • Watchouts: paywalled; image handling solid but toolchain thinner than GPT.

Google (Gemini)​

  • Models: Gemini 2.0 Flash (speed/cost), Gemini 2.0 Pro (quality); 1.5 remains available.
  • Strengths: low latency, good web/RAG fit, native on GCP/Vertex; Flash is cost king.
  • Watchouts: Pro can be slower; API quotas vary by account tier.

Mistral​

  • Models: Mistral Large 2, Small, Codestral Mamba for code.
  • Strengths: competitive latency and price; EU hosting options.
  • Watchouts: evals trail frontier models for deep reasoning.

Other notable​

  • xAI Grok, Cohere Command R+, AWS Bedrock as a hub (Anthropic, Mistral, Meta, Cohere), Azure OpenAI for enterprise/regional needs.

Open-weight options (self/managed)​

  • Llama 3.3 70B: strong generalist; needs ≥2×A100 80GB or managed inference (Fireworks, Together, Modal).
  • Qwen2.5 72B: great value; good at code and multilingual.
  • Mistral Large 2: closed weights via API; Mixtral variants for partial openness.
  • Small fast locals: Llama 3.3 8B, Gemma 2 9B, Phi-4 Mini. Run with vLLM or llama.cpp for CPU.
  • Guardrails: add Llama Guard, prompt audits, and per-request filters if self-hosting.

Benchmarks & where to check​

  • LMSYS / Chatbot Arena (live human rankings).
  • HELM, MT-Bench, vendor eval cards; treat numbers as directional.
  • Always run your own task-specific evals (RAG, codebase Q&A, red-team).

Tooling / routers​

  • OpenRouter, Fireworks, Together, AWS Bedrock, Vertex AI: one API, multiple models.
  • Good for A/B and fallback: start fast/cheap, retry with premium on low-confidence.
  • Watch for per-provider ToS on data retention; disable logging where supported.

Practical picking guide​

  • Product copy / emails: Claude 3.5 Sonnet or Haiku (cheap/fast); Gemini Flash for bulk.
  • Code chat / PR review: GPT-4.1 or Claude 3.5 Sonnet; Codestral/Gemma for cost-cutting.
  • Long docs Q&A: Claude 3.5 Sonnet or Gemini 2.0 Pro with RAG; avoid raw 1M-token dumps—chunk + retrieve.
  • Analytics/SQL help: GPT-4.1, Gemini Pro; add schema-aware RAG.
  • Fully offline / air-gapped: Llama 3.3 70B or Qwen2.5 32–72B with vLLM; accept quality gap vs frontier.

Costs (ballpark, USD, Dec 2025)​

  • Frontier premium: GPT-4.1, o1, Claude 3.5 Sonnet/Pro are highest per token.
  • Mid/fast: Gemini 2.0 Flash, Claude Haiku, Mistral Small, GPT-4.1 Mini = cheap enough for bulk.
  • Self-host: pay for GPUs instead of tokens; great for steady workloads, not for spiky usage.

Final notes​

  • Cache embeddings/results for repeated prompts.
  • Prefer RAG + short prompts over blind giant contexts.
  • Log prompt/latency/cost; set auto-fallbacks for 5xx/timeouts.
  • Re-evaluate quarterly: model quality and pricing move fast.