Best LLM for Every Task — 10 Tasks, 49 Models Ranked, Updated August 2026

Raw dataset: data.json. Cite this: All AI Ask LLM Task Recommendation Dataset, retrieved 2026-08-08.

For coding, Muse Spark 1.3 Contributor is our pick at $0.12/M tokens. For math & reasoning, GLM-5.2 is our pick at $3.40/M tokens. For chatbots & support, Muse Spark 1.3 Contributor is our pick at $0.13/M tokens. Every ranking below shows its formula, requirements, and evidence status — never a bare number.

"Fit" is a requirements match, not a quality benchmark. It combines price, measured speed, context window, and — where we have run it — graded accuracy on that exact task. Five of the ten tasks below carry first-party graded evidence; the other five are honest requirements-fit rankings and say so on the page.

Task	Our pick	Task price/M	Evidence	Eligible models	Best budget pick
Coding	Muse Spark 1.3 Contributor	$0.12	✓ 17 graded runs	49	Muse Spark 1.3 Contributor
Structured Data Extraction	Muse Spark 1.3 Contributor	$0.12	✓ 14 graded runs	49	Muse Spark 1.3 Contributor
Writing & Content	Muse Spark 1.3 Contributor	$0.15	✓ 14 graded runs	49	Muse Spark 1.3 Contributor
Math & Reasoning	GLM-5.2	$3.40	✓ 3 graded runs	28	Muse Spark 1.3 Contributor
Agents & Tool Use	Muse Spark 1.3 Contributor	$0.12	✓ 3 graded runs	28	Muse Spark 1.3 Contributor
Long Documents & RAG	Muse Spark 1.3 Contributor	$0.10	— requirements fit	40	Muse Spark 1.3 Contributor
Summarization	Muse Spark 1.3 Contributor	$0.10	— requirements fit	49	Muse Spark 1.3 Contributor
Chatbots & Support	Muse Spark 1.3 Contributor	$0.13	— requirements fit	49	Muse Spark 1.3 Contributor
Translation	Muse Spark 1.3 Contributor	$0.15	— requirements fit	49	Muse Spark 1.3 Contributor
Image Understanding	Gemini 3.7 Flash	$1.38	— requirements fit	37	Gemini 3.7 Flash

Data verified 2026-08-08. Every number is derived from our live pricing, speed, and graded-test datasets — see each task page for the exact formula.

How these rankings work

Batch 44 evidence surface · verified 2026-08-08 · frozen route allowlist: /best-llm-for

Workload routing, evidence sufficiency, and recommendation stability

Batch 44 · M1: Workload-to-owner compiler

Formula / rubric: qualified owner = exact task + artifact + scale + hard constraints match a published canonical owner.

Dated provenance: Frozen Batch 44 best-llm-for fixture; seven frozen workload intake fixtures and canonical child route inventory; reviewer ledger verified 2026-08-08.

First-party citation: All AI Ask route and evidence ledger

Field ID / fixture	Inputs	Observation / calculation	Decision boundary	State
`batch44-best-llm-for-m1-r1` coding agent	repository; multi-file edits; tests; tools; long horizon	Normalized to /best-llm-for/coding; sibling image-understanding owner rejected.	Hub routes; it does not emit a universal model winner.	PASS — canonical owner selected.
`batch44-best-llm-for-m1-r2` invoice extraction	PDF/image invoices; 12 fields; schema; citation trace; privacy hard constraint	No published owner covers privacy plus image-plus-schema in this exact combination.	Do not mint a thin page or borrow a nearby extraction verdict.	UNAVAILABLE — no qualified owner.
`batch44-best-llm-for-m1-r3` hard-reasoning brief	long text; citations; reasoning; 64K output; reviewer acceptance	Normalized to the hard-reasoning task owner; long-document sibling rejected on artifact mismatch.	Artifact mismatch prevents routing.	PASS — canonical owner selected.

Batch 44 · M2: Evidence-sufficiency map

Formula / rubric: coverage = observed required cells / required cells; winner suppressed when minimum coverage is not met.

Dated provenance: Frozen Batch 44 best-llm-for fixture; task-shaped runs joined to candidate coverage, freshness, price, speed, context, modality, and policy fields; reviewer ledger verified 2026-08-08.

First-party citation: All AI Ask route and evidence ledger

Field ID / fixture	Inputs	Observation / calculation	Decision boundary	State
`batch44-best-llm-for-m2-r1` multilingual support	required cells 12; observed 11; freshness 7d; pricing and speed joined	coverage = 11/12 = 91.7%; missing policy cell is blocking.	No winner is shown despite high numeric coverage.	UNAVAILABLE — policy field blocks.
`batch44-best-llm-for-m2-r2` image understanding	required cells 15; observed 15; modality packet and checker joined; freshness 14d	coverage = 15/15 = 100%; evidence minimum 90% is met.	Coverage qualifies evidence, not model quality beyond the task run.	PASS — child route may answer.
`batch44-best-llm-for-m2-r3` low-latency classification	required cells 10; observed 8; TTFT and quota joins absent	coverage = 8/10 = 80%; missing latency cells are not zero.	Unmeasured speed cannot become a penalty or win.	UNAVAILABLE — speed evidence insufficient.

Batch 44 · M3: Recommendation-stability and regret board

Formula / rubric: margin = top score − runner-up; stability requires same top candidate under the declared one-variable weight perturbation.

Dated provenance: Frozen Batch 44 best-llm-for fixture; five weight replays: quality, cost, speed, context, and privacy first; reviewer ledger verified 2026-08-08.

First-party citation: All AI Ask route and evidence ledger

Field ID / fixture	Inputs	Observation / calculation	Decision boundary	State
`batch44-best-llm-for-m3-r1` coding agent / quality-first → cost-first	baseline top A .82, runner-up B .79; cost replay A .74, B .76	Top changes A→B; baseline margin .03, replay margin .02.	Do not publish a stable recommendation when membership changes.	UNSTABLE — disclose regret path.
`batch44-best-llm-for-m3-r2` long-document synthesis / context-first	top C .88; runner-up D .81; context minimum 500K; both evidence-complete	C remains top; margin = .88−.81 = .07 after context-first weights.	Stability is workload-specific, not a global ranking.	STABLE — route to child owner.
`batch44-best-llm-for-m3-r3` privacy-first support	privacy fields missing for A and B; quality/cost scores present	Weight replay cannot be calculated because privacy exclusion is unknown.	Missing hard-constraint evidence suppresses the board.	UNAVAILABLE — no qualified top candidate.

Fail-closed rule: an unresolved identity, host, endpoint, artifact, modality, control, workload, acceptance, or accounting join remains Unavailable; no fallback or neighboring route supplies it.

Run the best-llm-for evidence canary →

Each task page defines hard eligibility requirements (a minimum context window, vision support, a reasoning mode) and a set of weights across price, measured speed, context window, and — where we have run a graded test — accuracy. Sub-scores are normalised within that task's eligible model set, never globally, and a model missing a measurement is never scored as zero — its weight is redistributed across what we do have. See "How we ranked this" on any task page for the exact numbers.

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