The table first
Per-answer prices are from the Whizi model cost index (1,000 input plus 500 output tokens, list rates fetched 2026-08-20). The strongest pick optimizes for quality; the value pick is what most people should actually route the task to.
| Task | Strongest pick | Value pick | Value pick, per answer |
|---|---|---|---|
| Writing a person will read end to end | Claude (Opus tier) | Claude Sonnet 5 | $0.007 |
| Code you will ship | GPT reasoning tiers | DeepSeek V4 Pro | $0.003 |
| Multi-step math and logic | GPT-5.6 Sol (reasoning) | DeepSeek R1 | $0.002 |
| Very long documents | Gemini 3.1 Pro, 1M context | Llama 4 Maverick, 1M context | $0.0006 |
| Data analysis on files | ChatGPT (code execution) | Same, then verify | $0.008 |
| Everyday questions | Any frontier everyday tier | GPT-5.6 Luna | $0.0008 |
| High-volume batch work | Not a frontier job | Llama 3.3 70B | $0.00026 |
| Image generation | Nano Banana Pro (Gemini 3 Pro Image) | Nano Banana 2 | $0.002 |
| Current-events research | A search-connected assistant | Gemini or Perplexity | varies with search |
Each row compresses an argument that runs much longer. The deep dives are one click down: data analysis, math, the open-source field, and the three-way frontier comparison.
Why the winners split by task
The splits come from how the models are built, not from their marketing, and that's why they survive version bumps.
Writing rewards taste. Claude's edge on prose that a person reads end to end has held across generations, which is why it anchors the writing row despite costing more than the everyday GPT tiers.
Code and math reward reasoning modes. Models that generate a long internal chain of work and check it before they answer dominate both rows. The open-weights entries, DeepSeek V4 for code and R1 for math-style reasoning, deliver most of that at a tenth of the price.
Long documents reward context length. A 1M token window, roughly 1,500 pages, is the difference between analyzing a contract and analyzing the first third of one. Several models now carry it, and it's the price per answer that separates them.
Volume work rewards cost. At $0.00026 per standard answer, Llama 3.3 70B does a thousand summaries for 26 cents. Being occasionally wrong is affordable at that price in a way it isn't at frontier rates 20 to 100 times higher.
How to read benchmark claims, including ours
Every model release ships with a chart where it wins. Three habits keep you honest.
Prefer task-shaped benchmarks over averages. A coding score like SWE-bench tells you about code; a blended leaderboard tells you about blends. The table above deliberately routes by task for this reason.
Check the date. The picks here were verified against the cost index and public reporting in August 2026, and this page updates as releases land. A "best model" claim without a date is folklore; the model timeline shows how fast the ground moves.
Run your own three prompts. Take one real task from your week and run it through the strongest pick and the value pick. Ten minutes of your own evidence beats any table, including this one, and how to choose an AI model turns that into a repeatable protocol.
Where this table is honestly uncertain
The top of every row churns. The strongest picks change with each major release cycle. The value picks are more stable, because the price floors move slower than the capability ceilings.
"Best" compresses variance. Claude wins writing on average; a specific GPT draft beats a specific Claude draft often enough that the cross-check habit matters more than the default.
Search-connected research depends on the search. For current events, the retrieval layer decides more than the model, so the research row names a category rather than a winner.
Your prompt is a bigger variable than your model. The gap between a vague and a specific prompt exceeds the gap between adjacent models on almost every task. The table optimizes the smaller variable; prompt basics optimizes the bigger one.
The subscription math this table implies
Follow the table literally with single-provider subscriptions and you're paying for ChatGPT Plus, Claude Pro, and a Gemini plan at roughly $20 each, about $60 a month, to get the best pick per row.
The alternative is one workspace that carries all the rows. Whizi Pro at $19.99 a month billed yearly includes ChatGPT Sol, Claude Sonnet, Claude Opus, Kimi K3, and Gemini Pro, plus value picks like DeepSeek and Llama for the volume rows, with the top reasoning tier on Powerhouse at $34.99. That's the whole thesis of a multi-model workspace: the table stops being a shopping list and becomes a routing rule inside one subscription. The subscription cost breakdown runs the comparison in detail.
- Route by task, not by brand loyalty: writing, code, long context, and volume have different winners
- Use the value pick by default and escalate to the strongest pick when quality visibly matters
- Send batch work to open models; frontier prices are for frontier problems
- Verify any benchmark claim against a task-shaped benchmark with a date
- Re-run your own three-prompt test after each major release cycle
Frequently asked questions
What is the best AI model overall in 2026?
There's no meaningful overall winner. The frontier models cluster within a few points on blended benchmarks while splitting sharply by task. Claude leads prose, reasoning tiers lead code and math, Gemini leads very long documents, and open models lead cost. The useful question is the per-task one, which is what the routing table answers.
Which AI model is best for coding?
For code you'll ship, the GPT reasoning tiers are the strongest pick, with DeepSeek V4 Pro as the value pick. DeepSeek's own reported 80.6% on the SWE-bench Verified benchmark sits in frontier territory, at about $0.003 per standard answer under an MIT license. For quick scripts and boilerplate, everyday tiers are fine and much cheaper.
Is the most expensive AI model the best one?
Only for the hardest tasks. Price tracks compute, and reasoning tiers genuinely earn their premium on multi-step problems. But the index spread from the cheapest to the priciest model is roughly 2,000x, while the quality gap on everyday questions is one you'll barely notice, so paying frontier rates for routine work is the most common way to overspend on AI.
How often does the best model change?
The strongest pick in a category changes roughly every major release cycle, a few times a year. The category structure changes much more slowly: writing has rewarded Claude, long documents have rewarded context length, and volume work has rewarded open models across multiple generations. Check the date on any recommendation, this page included.
Do I need multiple AI subscriptions to follow this table?
Not anymore. Following the table with single-provider plans costs about $60 a month across three subscriptions. A multi-model workspace carries every row in one plan: Whizi Pro at $19.99 a month billed yearly covers the frontier and value picks, Claude Opus included, with the top reasoning tier on the Powerhouse plan. It's the ability to switch models mid-conversation that makes per-task routing practical.
