Mercury 3 generates more than 1,000 tokens per second according to its provider, putting the speed of Artificial Intelligence (AI) in the spotlight. At the same time, cheaper search and extraction tools, models for older programming languages, and open systems for businesses and local computers are arriving. What matters to you is therefore not simply which model tops a ranking, but which one fits your task, budget, and requirements for handling data.
Comparing speed, price, and specialization
Mercury 3 is a Large Language Model (LLM), meaning a system that generates and processes language, but it does not write text one token at a time like conventional models. As a diffusion model, it begins with a rough version of the full response and refines multiple positions in parallel. Inception reports 1,068 tokens per second on unspecified NVIDIA graphics processors and a one-million-token context window, which provides room for very large inputs and conversation histories.
According to the Mercury 3 release report, the model leads GPT-6 Luna and Claude Haiku 5.5 on eight of 12 tests run by its maker. Its largest advantage appears in the SWE-Bench Atlas software test, while it trails Claude Haiku 5.5 on DeepSWE v1. These results have not been independently verified, and the report says all models used medium settings.
That speed could be noticeable in a voice assistant expected to respond with little delay during a conversation. A search tool that needs to summarize many results may also benefit from faster output. For an occasional email or summary, however, the difference between fast and extremely fast may matter less than reliability, ease of use, and price.
An Application Programming Interface (API) connects a model to another application. Mercury 3 is available only through an API, with no open model weights or smaller versions for a personal computer reported. The available source also provides no price, so its high output speed cannot yet be treated as evidence of lower costs.
Different models for different tasks
The other releases show that the market is moving beyond general-purpose chat models. OpenAI has improved GPT-6 for programming languages with relatively little available training code, including COBOL, Fortran, and Visual Basic. These languages still support critical systems: according to OpenAI and IBM, COBOL underpins more than 40 percent of online banking systems and 95 percent of ATM transactions.
In a test that required building a SQLite-compatible database engine, GPT-6 Astra achieved an average test pass rate of 87.4 percent across 20 languages, compared with roughly 38 percent for GPT-5.6 Terra. GPT-6 Luna scored 40.3 percent, only slightly above GPT-5.6 Terra at 37.9 percent, but it is described as substantially cheaper. For migrating COBOL into newer languages, GPT-6.1 Sol reached between 86.6 and 97.9 percent accuracy depending on its reasoning effort, while GPT-5.6 Sol scored 5.4 percent.
This matters mainly to organizations that need to inspect and gradually modernize older banking platforms or other long-running software. One concrete example is translating a COBOL program into Java, Python, or Rust. High test scores do not remove the need for specialist review or tests against the existing system.
Exa 3.0 focuses instead on web search through an API. It includes a larger embedding model, which represents content mathematically for retrieval, and Dynamic Highlights for selecting relevant passages. Exa Lite costs $4 per 1,000 requests and leads Perplexity, Brave, and Parallel across eight tests conducted by Exa itself, according to the summary of the upgraded search API.
Cloudflare targets an even narrower use case with Clairic. The preview extraction model reads information from an input and returns structured values in a defined format, together with confidence scores. A business could use it, for example, to extract amounts or other specified details from documents rather than asking a general chatbot for a free-form summary.
Clairic requires only one processing pass, according to Cloudflare. Its announced price is $0.10 per million input tokens, with no charge for output. Because the product remains a preview and the published details about Clairic are limited, its practical accuracy and constraints outside selected tasks remain unclear.
Pros and Cons of the new model variety
Pros:
- Higher speed – Mercury 3 can produce more than 1,000 tokens per second according to its provider, potentially making interactive applications feel more responsive.
- Lower entry costs – Exa Lite, Clairic, and MiniMax M3.1 Flash publish prices that make limited trials easier to budget.
- Purpose-built tools – Specialized models for search, extraction, or older programming languages may fit a task better than a general chatbot.
- More control – NVIDIA’s open models can run on an organization’s own infrastructure and be further adapted with its data.
Cons:
- Provider benchmarks – Several leading results come from the vendors themselves and have not been independently confirmed.
- Incompatible measurements – Tokens per second, accuracy, benchmark points, and cost per task cannot be compared directly.
- Errors remain costly – A fast or inexpensive model can still produce false information, with greater consequences in financial, document, or legacy-system work.
- Unclear availability – Not every source specifies supported languages, regional access, or conditions for processing sensitive information.
MiniMax M3.1 Flash illustrates the trade-off particularly well. The model scores 42 on the Artificial Analysis Intelligence Index, 13 points more than its predecessor, while costing less than half as much at roughly $0.22 per task. MiniMax also says that it is three times faster than M3.
At the same time, the report gives it a 93 percent hallucination rate because the model often attempts to answer even when uncertain. A hallucination is a plausible-sounding statement that is false or unsupported. Its attractive price therefore comes with a substantial reliability risk that raw speed and ranking figures do not reveal.
Costs and access at a glance
MiniMax M3.1 Flash accepts text and images and supports a context of up to one million tokens. According to the report on its pricing and test results, the API costs $0.15 per million input tokens and $0.60 per million output tokens, while cached inputs cost $0.015 per million tokens. MiniMax also announced that it would release the model weights openly on October 23, allowing them to be downloaded and operated outside the provider’s platform.
The benchmark picture is mixed. M3.1 Flash scores 48 percent on Humanity’s Last Exam and answers 56 percent of questions correctly in AA Omniscience. DeepSeek leads MiniMax by 45 to 42 points on the Intelligence Index and by 24 to 22 percent on the CritPt physics test. In the latter test, however, MiniMax remains ahead of GLM-5.3 Flash at 15 percent and Gemini 3.8 Flash at 18 percent.
NVIDIA is also pursuing an open approach. Nemotron 3.5 Super VL has 120 billion parameters and is intended for always-on enterprise agents, meaning systems that use tools to complete tasks with a degree of autonomy. Two smaller Bolt models are designed to run locally on new AI PCs using NVIDIA RTX Spark and on DGX Spark workstations.
NVIDIA claims that Super VL, after additional training for particular tasks, works faster than models from Anthropic and OpenAI. The named workloads include document analysis, financial accounting, scientific experiments, medical insurance claims, and resilience against security attacks. NVIDIA says companies including EY, Ramp, CrowdStrike, Perplexity, and Salesforce are already involved, but the performance claims about Nemotron remain provider statements.
Pricing is therefore incomplete and presented in incompatible formats. Exa charges per search request, Clairic and MiniMax charge by tokens, and the available reports give no directly comparable cost for Mercury 3 or NVIDIA’s models. The lowest list price is not necessarily the lowest total cost if a model produces more output, requires more checking, or depends on additional hardware.
Practical value for beginners and advanced users
If you are getting started, begin with a narrow task whose result you can verify. You might run a small number of searches with Exa Lite or use an extraction model to identify defined fields in a few non-sensitive documents. Compare completeness, errors, and actual costs rather than measuring speed alone.
If you have more experience, run the same task across several models using fixed criteria. For a search assistant, those could include result quality, relevance of highlighted passages, response time, and price per request. For a COBOL migration, working tests and accurate transfer of the program logic should be added to the evaluation.
For users in Switzerland, the location of data processing and the option to run a model locally or on private infrastructure may also matter. NVIDIA’s open models and the announced MiniMax weights provide more technical control in principle, while Mercury 3 is accessible only through the provider’s API. The sources do not provide Switzerland-specific availability, confirmed data locations, support for the country’s languages, or detailed privacy terms.
The releases point to a clearer division of labor: Mercury 3 prioritizes speed, OpenAI targets older programming languages, Exa handles search, Clairic extracts structured information, and NVIDIA emphasizes controlled deployment. MiniMax combines low prices with notable benchmark scores but also carries a striking hallucination risk. Trials make the most sense for tasks with measurable results, while the reliability of provider claims under real operating conditions and with sensitive data remains unresolved.
Sources
- Inception Releases Mercury 3 Diffusion LLM at 1,068 Tokens per Second – MarkTechPost, 2026-10-20
- OpenAI macht GPT-6 fit für alte Programmiersprachen wie COBOL und Fortran – The Decoder, 2026-10-20
- Exa 3.0 Search API Tops Perplexity, Brave and Parallel on 8 Benchmarks – MarkTechPost, 2026-10-20
- Cloudflare Releases Clairic, a System One Extraction Model on Workers AI – MarkTechPost, 2026-10-20
- MiniMax M3.1 Flash ist Chinas neuestes Modell, das KI-Preise Richtung Keller drückt – The Decoder, 2026-10-19
- Nvidia launches new open models to run agents in the enterprise and on PCs – TechCrunch, 2026-10-19
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