A universal “best AI model” table is attractive because it converts a complex market into one winner. It is also the fastest way to make a bad purchase. Current models are differentiated by reasoning depth, speed, context, multimodal input, tool use, live information, deployment route and price. A model can be exceptional for a long coding task and wasteful for extraction; another can be inexpensive enough to run at scale but wrong for a consequential open-ended decision.
The comparison below does not rank intelligence. It maps ten current APIs to the job their own documentation most clearly positions them to perform, then gives the evaluation question that could confirm or reject that fit. Published price is included because the shape of a model family is partly economic.
Ten leading APIs as ten different hiring decisions
| Model | Documented positioning and standard price | Test that matters |
|---|---|---|
| GPT-6 Astra | Frontier reasoning; $10 input / $50 output per 1M tokens | Does the extra capability reduce corrections on the hardest long-horizon work?Source 1 |
| Claude Fable 5.1 | Demanding reasoning and long-horizon agents; $10 / $50 | Can it maintain a plan, evidence and state over a task that defeats shorter runs?Source 2 |
| Claude Opus 5.5 | Long-running agentic coding and knowledge work; $4 / $20 | Does it complete a repository-scale change with fewer human interventions?Source 2 |
| Gemini 3.1 Pro Preview | Multimodal and agentic work; $2 input / $12 output at ≤200K prompt tokens; $4 / $18 above 200K | Can it use the decisive evidence across text, image, audio or video—not merely accept the media?Source 3 |
| Grok 4.7 | Coding, agents and knowledge work; $2 / $6 before higher-context pricing | Does its live-search environment improve a current question without lowering source quality?Source 4 |
| Kimi K3 | One-million-token flagship for software engineering, knowledge work and deep reasoning; $3 / $15 | Can it locate and use the exception hidden deep inside a very large source set?Source 7 |
| Qwen3.8-Max | Flagship API; $1.65 / $4.951 on documented Global-scope routes; Singapore International $2 / $6 | How well does it perform on the target language, region and tool stack at the stated endpoint?Source 6 |
| GLM-5.3 | General model at $1.40 / $4.40; lower-cost Flash family also available | Does the selected family hit the quality threshold without paying for unused capacity?Source 9 |
| Mistral Large 3 | General model at $0.50 / $1.50 with low cached-input pricing | Can it deliver acceptable structured work cheaply enough for repeated production use?Source 8 |
| DeepSeek V4.1 Flash | One-million-token thinking/vision model; $0.30 / $1.20 peak, half off-peak | Can the workflow exploit the price advantage while preserving reliability and availability?Source 5 |
The interesting comparison is the failure each model prevents
For high-end reasoning, the failure may be abandoning a long plan, losing a constraint or producing a plausible answer that cannot survive review. For a long-context model, it may be overlooking one clause. For a live-search product, it may be using stale information. For a value model, it may be exceeding the budget before enough attempts can be run. These are different loss functions; one benchmark average cannot express them.
Five job classes and the evidence they need
| Job | Candidate families worth testing | Acceptance evidence |
|---|---|---|
| Frontier research or strategy | Astra, Fable, Gemini Pro | Correct sources, coherent long-horizon reasoning and conclusions that survive expert reviewSource 1Source 2Source 3 |
| Repository-scale coding agent | Opus, Astra, Grok, Kimi | Tests pass, requirements are traced, tool calls stay within scope and human interventions fallSource 2Source 1Source 4Source 7 |
| Huge-document investigation | Kimi, DeepSeek, Gemini Pro, Qwen | The system finds the decisive passage and cites its exact location across long inputsSource 7Source 5Source 3Source 6 |
| High-volume extraction and routing | DeepSeek Flash, GLM Flash, Mistral, Claude Haiku | Field accuracy, latency and cost at production volume—not conversational impressivenessSource 5Source 9Source 8Source 2 |
| Current-events research | Grok or any model connected to high-quality search and sources | Freshness, primary-source selection, citation accuracy and correction of conflicting reportsSource 4 |
Price creates a portfolio, not a podium
The table reveals a roughly 37-fold range between the hypothetical DeepSeek Flash peak invoice and Astra or Fable for the same 50K-input, 10K-output token counts. That does not mean the cheapest model is 37 times better value. It means a rational system can route routine work to inexpensive models and reserve the frontier model for tasks where it changes the answer.
A useful routing policy begins with the lowest-cost model that has passed the task’s acceptance test. Escalate when a validation fails, required evidence is missing, a known failure pattern appears or a calibrated score crosses the task’s threshold. A confident tone or an unvalidated self-rating is not a reliable routing signal. Record the reason so that extra spending can be connected to a better accepted outcome.
Do not compare labels without the service around them
The model name is only one variable. Reasoning effort, context band, cache state, tools, regional endpoint and provider interface can change quality, speed and price. Grok 4.7, for example, has higher-context pricing beyond 200,000 tokens. Astra reprices the full request above 272,000 input tokens. DeepSeek changes price by time of day. Google and Anthropic publish distinct cache and batch rates.[4][1][5][3][2]
The market’s diversity is not noise to be simplified away. It is the useful fact. Frontier models, long-context systems and low-cost engines are becoming different instruments. The better question is no longer “Which model is smartest?” It is “Which failure is expensive here, and which model-and-workflow combination prevents it most economically?”
Sources & further reading
- GPT-6 Astra model documentation OpenAI · Accessed 9 Oct 2026
- Claude Platform pricing Anthropic · Accessed 9 Oct 2026
- Gemini Developer API pricing Google AI for Developers · Accessed 9 Oct 2026
- Grok 4.7 model documentation xAI · Accessed 9 Oct 2026
- DeepSeek models and pricing DeepSeek · Accessed 9 Oct 2026
- Qwen3.8-Max model information and pricing Alibaba Cloud Model Studio · Accessed 9 Oct 2026
- Kimi API platform models and pricing Moonshot AI · Accessed 9 Oct 2026
- Mistral model pricing Mistral AI · Accessed 9 Oct 2026
- Z.ai model pricing Z.ai · Accessed 9 Oct 2026