Which LLM for my use case?
Answer 7 questions. Get a ranked shortlist from Claude, GPT, Gemini, Llama, Mistral, DeepSeek and more — with cost estimates for your volume. Deterministic, no sign-up, no inference calls.
The frontier LLMs leading April 2026
Verified against arena.ai/leaderboard on 2026-04-19. Take the quiz below for a pick tailored to your use case and volume.
What are you building?
Pick the closest match. You can change it in a second.
Frequently asked questions about picking an LLM
Quick answers about choosing the best LLM in 2026 — how to compare Claude, GPT, Gemini, Llama, Mistral, and DeepSeek across coding, RAG, agents, vision, maps, and cheap production use cases. Expand any question to read the full answer. Last reviewed 2026-04-19.
How do I choose the right LLM for my use case?
Choose an LLM by filtering on your hardest constraint first — context window, compliance, or latency — then ranking the survivors by benchmarks that match your task and cost at your monthly volume. Relevant benchmarks differ by task: SWE-bench for coding, needle-in-haystack for RAG, MMMU for vision, τ-bench for agents. The LAXIMA LLM Picker automates this in 7 questions and returns a ranked shortlist with monthly cost estimates and the score breakdown behind each pick.
Which LLM is best for coding in 2026?
As of April 2026, Claude Sonnet 4.6 is the best general-purpose coding LLM — it leads SWE-bench Verified and agentic-coding benchmarks while costing $3 / $15 per 1M tokens. Claude Opus 4.7 ($5 / $25) edges it out on the hardest tasks, and GPT-5.4 is a close third with strong tool use and fine-tuning support. For IDE-style fill-in-the-middle and autocomplete, Codestral 25.08 ($0.30 / $0.90 per 1M) is the best price/performance option with a 256K context window for large codebases.
Which LLM is best for RAG?
Gemini 3.1 Pro leads for RAG in 2026 because of its 2M-token context window — the largest available from any frontier lab — and strong needle-in-haystack recall. Claude Sonnet 4.6 is a strong second with 1M context, prompt caching (up to 90% cost cut on repeat prompts), and excellent grounding. Cohere Command A is purpose-built for enterprise RAG and is the top choice when on-prem deployment is required.
Which LLM is best for AI agents and tool use?
Claude Sonnet 4.6 is the best LLM for autonomous agents and tool use in 2026, leading τ-bench and real-world multi-step tool-calling benchmarks. Use Claude Opus 4.7 for the hardest agent loops where reasoning depth justifies the cost; use GPT-5.4 when you need OpenAI's tool ecosystem (Code Interpreter, File Search, Built-in Retrieval).
Which LLM is cheapest for production in 2026?
Gemini 3.1 Flash-Lite at $0.25 / $1.50 per 1M tokens is the cheapest credible frontier-lab LLM in April 2026, followed by GPT-5.4 Mini ($0.75 / $4.50) and Claude Haiku 4.5 ($1 / $5). At high volume, prompt caching matters more than list price — it cuts repeated-prompt cost by 80–90% and is available on Claude, GPT-5, and Gemini. For open-weights / self-hosted workloads, DeepSeek V3.2 is cheapest at $0.28 / $0.42 per 1M (just $0.028 with caching) when China data-routing is acceptable; Qwen 3.6 Plus is the best open-weights option for the rest of the world.
Which LLM is best for maps and geospatial applications?
Gemini 3.1 Pro is the best LLM for maps and geospatial apps because Google's knowledge graph and Maps-adjacent training data give it a structural advantage in place names, routing, and travel reasoning. Gemini 3 Flash gives you the same geo advantage at production cost ($0.50 / $3 per 1M). Claude Sonnet 4.6 is a solid fallback when you need stronger tool use to chain Google Maps Platform APIs.
What is the best open-weights LLM in 2026?
Qwen 3.6 Plus is the best general-purpose open-weights LLM as of April 2026 — 1M-token context window, strong multilingual performance, and vision support. DeepSeek V3.2 is the leading cheap open-weights reasoning model at $0.28 / $0.42 per 1M tokens when China data-routing is acceptable. GLM-5.1 (Z.ai) is the top open-weights frontier coder, ranked #15 on arena.ai/leaderboard. Codestral 25.08 remains the best open-weights coding specialist with a 256K context. Meta's Llama 4 family is excluded from this picker because it underperforms current open-weights leaders on independent benchmarks.
Do you use an LLM to pick the LLM?
No — the picker is fully deterministic. It combines hard filters (modality, context window, compliance), soft scoring (benchmarks weighted by use case), and a curated editorial layer per use case. Every recommendation shows the full score breakdown. No inference calls are made, which means your answers stay on your device and the same inputs always produce the same ranking.
How fresh is the LLM catalog?
The catalog was last audited 2026-04-19 and every model entry carries a visible "last verified" date. New frontier models are added within a week of release; pricing and capability claims are reverified monthly. If a model's verification date is more than 90 days old, treat pricing and capabilities as stale and confirm on the provider's page.
How are the monthly cost estimates calculated?
Cost estimates combine four inputs: (1) your selected monthly call volume, (2) typical token shape per use case — for example RAG averages 8k input / 600 output tokens per call, coding averages 3k / 800, (3) published provider pricing per 1M tokens, and (4) realistic prompt-caching assumptions for models that support it. Batch-eligible workloads apply the provider's 50% batch discount when you select "batch is fine" for latency. Figures are ballpark, not contractual — always run a pilot before committing to production.
Why does the picker sometimes override the benchmark winner?
Editorial picks override the benchmark leader when a model has provider-specific strengths that generic benchmarks miss. Gemini 3.1 Pro wins for maps because of Google's knowledge graph; Codestral 25.08 wins for IDE autocomplete because of its fill-in-the-middle training; Qwen 3.6 Plus wins for Chinese translation because of its native multilingual training. Editorial boosts are explicitly labeled in the result card with the reason shown, so you can always see when an opinion is being applied and decide if you agree.