How to Choose an LLM in 2026: Best by Use Case

Choose the failure first, then the model. A coding assistant that writes elegant prose but breaks builds is a bad coding assistant. An agent that scores well overall but needs constant intervention is a bad agent. A frontier API that costs more than the workflow saves is a bad business decision.

We use the overall ranking as a shortlist, not a purchase order. Use the relevant capability view, check evidence strength and access, then test the top two affordable candidates on your own work.

The 60-second answer

Table 1 Download CSV

Need	Start with	Why	Main caveat
Highest capability	Claude Mythos 5	#1 overall, coding, and agentic; Supported	Restricted access
Best generally available model	Claude Fable 5	#2 overall, coding, and agentic; Supported	$10/$50 per 1M tokens
Agentic value	GPT-5.6 Sol	#3 agentic at 75.49 Supported	Overall and coding are Estimated
Budget general use	Gemini 3.5 Flash	75.02 overall Supported at $1.50/$9	Agentic score is much lower
Open-weight control	MiniMax M3	#1 open overall at 69.8 Supported	Trails the proprietary frontier broadly
Open coding	GLM-5.2 or Kimi K2.7 Code	Top current open coding rows	GLM-5.2 is Estimated; validate both

Start with the task

Coding

Claude Mythos 5 leads the coding ranking at 81.95, followed by Claude Fable 5 at 81.7. Mythos is restricted, so Fable is the public starting point. GPT-5.6 Sol is third at 74.08 with Estimated evidence; Claude Opus 4.8 is fourth at 73.49 with Supported evidence.

The 0.59 points between Sol and Opus 4.8 do not settle a repository decision. Put both against real issues from your codebase. Measure accepted patches, tests passed without repair, regressions, review time, and token cost. A model that needs one fewer repair loop can be cheaper even at a higher list price.

→ Current coding ranking

Agents and tool use

Mythos, Fable, and GPT-5.6 Sol are the top three agentic rows at 77.09, 76.84, and 75.49. All three have Supported evidence. Sol is the practical value candidate at $5/$30, versus $10/$50 for Fable and restricted Mythos.

Do not optimize an agent for task score alone. Track intervention rate, recovery after a bad tool call, permission errors, time to completion, and cost per successful run. The best model is the one that completes your workflow safely, not the one that begins it most impressively.

→ Current agentic ranking

General knowledge work

Use the overall ranking to build the shortlist. Mythos leads at 83.85, Fable follows at 83.6, and GPT-5.6 Sol is third at 79.3. Access removes Mythos for most users. Price may remove Fable. That leaves Sol, Opus 4.8, Gemini 3.5 Flash, and other top-ten rows as reasonable candidates depending on the workload.

Gemini 3.5 Flash deserves a specific look for high-volume summarization, extraction, research preparation, and document workflows. Its 75.02 overall row is Supported and its price is $1.50/$9. Its agentic score is 49.1, so keep long autonomous chains out of the default plan unless your evaluation proves otherwise.

Writing and conversation

The current catalog does not justify a universal creative-writing winner. Public writing data is thinner, more preference-sensitive, and easier to contaminate than coding or agentic evidence. Claude may still be the right first candidate, but that is a hypothesis to test.

Create a blind set of briefs, rewrites, edits, and difficult tone corrections. Have the people who own the voice grade adherence, originality, edit distance, and factual discipline. Do not borrow a coding rank as proof of writing quality.

Math, multilingual, and multimodal work

Treat these as lenses. Select the models with direct evidence for the exact modality or language, then evaluate on representative inputs. A text-only configuration should not lose the core text-model rank because it cannot see an image, but modality support should be explicit in a use-case recommendation.

For multilingual work, test your actual language pairs and domains. An aggregate can hide a model that is excellent in French and weak in Japanese. For multimodal work, distinguish image understanding, document extraction, charts, video, and computer use; they are not interchangeable capabilities.

Choose the operating model

Proprietary API

Choose an API when peak capability, rapid upgrades, and low infrastructure overhead matter. The costs are vendor dependence, variable behavior after model updates, and less control over data handling. Keep an evaluation gate between a provider update and production traffic.

Open weight

Choose open weight when data must stay inside your environment, you need fine-tuning or serving control, or sustained volume can justify infrastructure. MiniMax M3 leads the current open overall ranking at 69.8 with Supported evidence. GLM-5.1 follows at 67.76.

Open weight is not automatically cheaper. Include GPUs, idle capacity, engineering time, observability, safety controls, and upgrades. It can still be the only acceptable answer when control is part of the requirement.

→ Best open-weight models · Self-host calculator

Read the evidence label

Supported and Estimated are not separate leagues. They are confidence signals attached to one ranking.

• Supported means the row has enough independent evidence to carry normally.
• Estimated means the available evidence can place the model, but the uncertainty is wider.

An Estimated model can be excellent. GPT-5.6 Sol is third overall. The label tells you to avoid false precision and prioritize a direct trial. Missing results do not become zeros, so a newly released model can rank without being punished for a different disclosure table.

Compare total cost, not token price

For one million input and 200,000 output tokens, list-price cost is roughly $20 for Claude Fable 5, $11 for GPT-5.6 Sol, and $3.30 for Gemini 3.5 Flash. That spread matters. It is still incomplete.

Add retries, human review, failed runs, latency, caching, and the cost of an incorrect answer. If Fable cuts intervention enough, it can beat a cheaper model. If Gemini handles a high-volume extraction task correctly, paying for frontier agent capability is waste.

→ Live pricing · Cost calculator

Run a small evaluation before committing

1. Collect 30 to 100 real examples, including failures and edge cases.
2. Define acceptance before seeing model names.
3. Compare two or three available models within budget.
4. Blind the outputs where human preference is involved.
5. Measure success rate, intervention, latency, and cost per successful task.
6. Repeat after meaningful provider or prompt changes.

The live ranking reduces the search space. Your evaluation makes the decision.

Current defaults

Use Mythos for peak capability when you have access. Use Fable as the strongest generally available broad and coding default. Use GPT-5.6 Sol when agentic performance and cost need to balance. Use Gemini 3.5 Flash for budget-sensitive general workloads. Start with MiniMax M3 when open-weight control is required.

Those are starting points, not permanent endorsements. The models, prices, and evidence will change. The selection process should not.

→ Live leaderboard · Compare models · Model selector