OpenBioLLM Alternative: Mira-Q2 vs a 70B Medical Model

A Head of Data Science shortlisting open models for clinical document work may well come across OpenBioLLM. It is an open biomedical model on Hugging Face, its developer reports an average of 86.06% across nine biomedical datasets, and at 70 billion parameters it looks like the serious option. Next to it, a 3B extraction model can look like a toy. That impression is the thing worth testing, because the two models were built for different jobs and are scored with different measures.

This guide sets Dilr Mira's live model, Mira-Q2, beside OpenBioLLM-70B and its smaller sibling OpenBioLLM-8B on the facts each publishes: size, base model, training hardware, what the headline numbers measure and what each developer says the model must not be used for. It then gives a head-to-head test a team can run on its own documents. The scope is deliberately narrow. The general contrast between biomedical models and extraction models, and the Meditron advisory, belong to our clinical document extraction guide; the full Mira-Q2 scorecard belongs to the 782-document evaluation. This post holds the OpenBioLLM comparison and the method for reading a biomedical benchmark table.

This guide is shipped by the team behind Dilr Mira, a class of private clinical small language models that turn scans, lab reports and claim forms into source-grounded, schema-valid JSON on your own hardware. Or see DATS, our consulting and delivery practice, which is how Mira reaches a regulated production environment.

What is OpenBioLLM, and what is it built to do?

OpenBioLLM is a family of open biomedical language models from Saama AI Labs, released on Hugging Face in April 2024 in two sizes, 70 billion and 8 billion parameters. OpenBioLLM-70B is fine-tuned from Meta-Llama-3-70B-Instruct with preference optimisation and a custom medical instruction dataset. It is a general biomedical model offered for a wide range of tasks, from medical question answering to summarising clinical notes, rather than a model trained to one fixed output schema.

The OpenBioLLM-70B model card is candid about what the model is for. Alongside question answering, it shows worked examples of summarising clinical notes, clinical entity recognition and de-identification, so extraction-like work is within its intended range. The card notes that those examples come from a quantised version of the 70B model. What the card does not publish is an extraction metric. Its benchmark table is accuracy on nine named datasets, and its training section records the hardware used: eight H100 80GB GPUs with a QLoRA adapter. That is a training figure, not a statement of what you need to run the model, and the two should not be confused when sizing a deployment.

The card also says the accompanying paper is in progress, and it includes an advisory notice. The developer advises against using OpenBioLLM-70B for direct patient care, clinical decision support or other professional medical purposes, describes it as a research tool, and notes that its performance has not been evaluated in randomised controlled trials or real-world healthcare settings. That is a responsible position, and it frames everything below. The two developers set different limits: OpenBioLLM is offered for research, development and exploratory use, while Mira-Q2 is offered for extraction whose every output is a draft for human review. Neither is a decision-maker. For how we think about placing models inside workflows, see our approach.

How do Mira-Q2 and OpenBioLLM compare on size, hardware and licence terms?

Mira-Q2 and OpenBioLLM sit at opposite ends of the scale. Mira-Q2 is a clinical extraction model of about 3B parameters built on Qwen2.5-3B-Instruct, published on Hugging Face, running on CPU and taking about 2 GB on disk, with a GPU only adding speed. OpenBioLLM-70B is a 70 billion parameter biomedical model built on Llama 3, with an 8 billion parameter sibling for smaller budgets.

The table sets out what each developer publishes. Every cell comes from the relevant model card or the Dilr Mira models page, except the purpose row, which is our summary of each card. The 8B column comes from the OpenBioLLM-8B model card.

Two cautions about that table. First, the hardware rows are not like for like. The OpenBioLLM cards record the GPUs used for training, but neither states an inference footprint, so any sizing has to be done by the buyer. As a rough guide, and this is our arithmetic rather than a figure from the card, 70 billion weights held at 16 bits each come to about 140 GB before any working memory, which puts the full model beyond an ordinary workstation unless it is heavily quantised. That is a 16-bit figure, so it is not like for like with Mira-Q2's published 4-bit footprint of about 2 GB, which the Dilr Mira models page says runs on CPU, as our Dilr Mira explainer also sets out. Sizing either model properly means testing it at the precision you intend to deploy.

Second, licence terms belong in the evaluation, and they are not a box to tick from a summary. OpenBioLLM-70B is distributed under the Meta Llama 3 licence, which carries its own conditions, including a separate licence request for a licensee whose products exceed 700 million monthly active users. For every fine-tuned open model on a shortlist, legal should read the model's own licence and the licence of the base model it was fine-tuned from before a commercial deployment is approved. We say that for every model on a shortlist, ours included. Licence review sits well inside an AI operating model, which covers governance, RACI and lifecycle.

What does OpenBioLLM's 86.06% average actually measure?

OpenBioLLM-70B's 86.06% is a developer-reported average accuracy across nine datasets named on its model card: Clinical KG, Medical Genetics, Anatomy, Professional Medicine, College Biology, College Medicine, MedQA, PubMedQA and MedMCQA. Each is scored as accuracy. The figure says how often the model picks the right answer; it says nothing about whether the model returns a valid, grounded record from a document.

Reading a biomedical benchmark table well takes three questions, and the OpenBioLLM card answers all three if you read the small print.

What is the metric? Accuracy on question sets. A clinical extraction workflow needs a different property: that the output parses against a schema, that every field traces to a span of the source document, and that no patient identifier leaks into the output. A high knowledge score is evidence that a model has absorbed medical language. It is not evidence that it will fill a lab-report schema correctly on a scanned referral.

Under what conditions? The card is explicit, and it is worth quoting exactly because the comparison rows depend on it: "All results presented are in the zero-shot setting, except for Med-PaLM-2 and Med-PaLM-1, which use 5-shot accuracy." The card also says the GPT-4, Med-PaLM-1 and Med-PaLM-2 results are taken from their official papers. So the table mixes evaluation settings and mixes results produced by different teams. That is disclosed honestly, and a careful reader should weigh it: the rows are not all one controlled experiment, which is why a reproducible evaluation harness matters more than any single table.

Who ran it? The developer. In the same table, OpenBioLLM-8B averages 72.502 and Meditron-70B shows 64.52. Those figures may well be right, but they are self-reported in a table the developer compiled, and the card says its accompanying paper is in progress. Treat them as a reason to shortlist, not as a result to cite in a business case. A claim earns weight when the buyer can reproduce it, and an AI placement diagnostic produces a ranked roadmap of where AI belongs and where it does not.

How is Mira-Q2 measured, and why is that a different number?

Mira-Q2 is measured on structured extraction, not on medical questions. Its published evaluation covers 782 documents across four test sets and reports JSON validity, identifier leakage and field-F1. Mira-Q2 reaches field-F1 of 1.000 on held-out gold data and records zero identifier leaks across all 782 documents. These are extraction measures, so they cannot be set against OpenBioLLM's accuracy scores.

The most useful part of the Mira-Q2 scorecard is the spread across test sets, because it shows where the model is strong and where it is not. The chart below uses one source and one measure, JSON validity, for all four bars.

The Mira-Q2 model card publishes the gap rather than hiding it: 100% validity on training-distribution data falls to 85.8% on general real physician prose from 39 specialties, and recovers to 94.7% on real documents that match the schema. That honesty matters for this comparison. A buyer choosing between a 3B extractor and a 70B generalist should know that the extractor is strongest on the document types it was trained on, lab reports, discharge summaries and medication lists, and weaker on free-form operative notes, a pattern our open models guide also flags.

The training story explains where the model's strengths come from. According to the Dilr Mira models page, Mira-Q2 is a QLoRA r=16 adapter trained on 8,400 examples, 6,400 gold-by-construction and 2,000 schema variants, with vocabulary grounded in real ICD-10 codes, NLM drug names and curated lab reference ranges. The model card adds that the training data are synthetic, which is why real physician prose scores lower. The same base model with no training scores 0% validity on held-out gold, because it invents its own schema, so at the same size the training is what separates the two. For the method behind those numbers, the 782-document evaluation post walks through each test set.

Which tasks favour OpenBioLLM, and which favour a small extractor?

OpenBioLLM is designed for open-ended biomedical language work in research settings: answering medical questions, summarising clinical text and exploring literature, where generated prose is the output. Mira-Q2 is designed for cases where the output is a fixed schema that a downstream system will ingest, the documents resemble lab reports, discharge summaries or medication lists, and the data must stay on the organisation's own hardware.

The split follows the shape of the output, not the medical domain. A useful way to sort a backlog of use cases is to ask what happens to the answer next.

• A researcher reads it. A literature digest, an answer to an exploratory medical question, a summary of de-identified notes for a study. Fluency and breadth of medical knowledge matter most, and a general model such as OpenBioLLM-70B is designed for that kind of research and exploratory use, which is where its own card confines it.
• A system ingests it. A coded lab value posted to a results table, a medication list reconciled against a formulary, a claims field validated before payment. Here the output must parse, every value must trace to the source, and identifiers must not leak. That is the job Mira-Q2 is trained and measured for, and its verifier-gated workflow checks schema, grounding and leakage before a person reviews the draft.
• Both. Some workflows extract first and summarise second. Nothing stops a team using a small extractor for the structured record and a larger model for narrative, provided each sits behind the same review and audit controls.

Where data may run matters as much as the task. Health data is special category data under UK GDPR Article 9, whose processing is prohibited unless a condition applies, so a team may want models that run inside its own estate. Both OpenBioLLM and Mira-Q2 are open weights that can, in principle, run on premises. The practical difference is cost of hardware: a model that fits on one ordinary computer can be placed beside the data in places a 70 billion parameter model cannot easily go. That is the same perimeter argument our health insurance claims extraction guide makes for insurers.

The same question of what happens to an output sits behind our enterprise AI solutions, six named solutions that include evaluation and observability for production agents.

How should a team run a head-to-head on its own documents?

A fair head-to-head between Mira-Q2 and OpenBioLLM uses the buyer's own schema and documents, not either developer's benchmark. Fix the schema first, draw a stratified sample of real documents, run both models under identical prompts and hardware limits, then score every output on validity, grounding and leakage, with a clinician reviewing a blind subset. The model that wins that test is the right one.

The flow below is the order we recommend. It is a method, not a measured result, and it applies to any pair of models.

Fix the schema. Write the exact JSON schema the downstream system needs, with required fields and types. Mira-Q2 takes the schema as an input, since it was trained on schema variants; a general model like OpenBioLLM will need the schema in its prompt and may need constrained decoding to stay inside it. Writing the schema first stops either model defining success.

Sample documents. Draw real documents stratified by type, including the hard ones: free-text operative notes, poor scans and mixed-format referrals. Mira-Q2's own scorecard shows why this matters, since its validity on general physician prose is lower than on lab reports. A sample of only clean documents flatters every model, and the team behind Dilr Mira publishes its own model's weaker real-prose result rather than hiding it.

Run both models. Use the same prompts, the same context limits and the hardware you would actually deploy on. If the 70B model only runs on a cloud GPU, record that, because it changes where the data goes and therefore your Article 9 analysis. For a longer programme, our execution office is embedded delivery, with production placements the client owns.

Score and review. Measure JSON validity, field-level precision and recall against a hand-labelled subset, grounding of each value to its source span, and identifier leakage. Then put a blind sample in front of a clinician. If you want a reusable harness for this, our note on evaluation harness engineering describes its five layers.

What do both models leave to the buyer: governance, review and data protection?

Neither developer offers its model for clinical decisions, though the limits differ. OpenBioLLM's card confines it to research and advises against professional medical use, while every Mira-Q2 output is a draft for human review. Whichever model a team chooses, it still owns the review step, the audit trail, the lawful basis for processing health data and the question of whether its use makes the software a medical device.

That is the most important line in this comparison, because it means the model choice does not move the governance burden. Four things stay with the buyer either way.

1. Human review. A named reviewer accepts or corrects each extracted record before it is relied on. The review rate can fall as evidence accumulates, but it does not reach zero for clinical data. More on review design sits in our AI strategy guides.
2. An audit trail. Each output needs a record of the document, the model version, the schema version, the verifier result and the reviewer's decision. Mira's models page describes a verifier gate and audit log; with any open model, the team must build and keep that record itself.
3. Data protection. Health data falls under Article 9, so the controller needs a lawful basis, an Article 9 condition and a clear answer to where the model runs. Running on premises simplifies that answer but does not remove the duty.
4. Medical device status. The MHRA's Software and AI as a Medical Device change programme is where the UK regulator sets out its work on software and AI as a medical device. Dilr Mira is an extraction tool whose outputs are drafts for human review, never autonomous clinical decisions. Whether a system built around either model needs device assessment is a regulatory question, so it belongs with the team's regulatory lead from the start.

None of this is specific to the model. It is why we treat model selection as one decision inside a wider AI operating model, and why our healthcare sector overview maps where AI pays and what DTAC, DSPT and CQC require before any model is chosen.

What is the best OpenBioLLM alternative for clinical extraction in 2026?

For schema-valid clinical extraction on an organisation's own hardware, a small, purpose-trained extraction model such as Mira-Q2 is the OpenBioLLM alternative to test first, provided the documents resemble its training types. For open-ended medical question answering or summarisation in research, OpenBioLLM is designed for the job, and a cloud service may suit a team that can lawfully send data off premises.

The verdict depends on five criteria, and a shortlist should score every candidate against all of them.

• Output shape. If a system ingests the output, favour a model measured on validity and grounding. If a person reads it, favour fluency.
• Published, reproducible evidence. Prefer models whose evaluation files you can rerun. Mira-Q2 publishes its eval files and scorecard on Hugging Face; OpenBioLLM publishes a benchmark table, its card lists an LM evaluation harness among its tools, and it says the accompanying paper is in progress.
• Deployment footprint. Size the inference hardware you would really use. A model that runs on CPU can sit beside the data; a 70 billion parameter model needs serious hardware or heavy quantisation.
• Fit to your documents. Run the head-to-head above. Mira-Q2 is weaker on free-form physician prose than on lab reports, and that may decide the matter for an operative-notes workflow.
• Licence and support. Read the model and base-model licences, and decide who supports the model in production, whether an internal team or a partner such as DATS. Commercial clinical NLP from John Snow Labs and cloud services such as AWS Comprehend Medical or the Google Healthcare Natural Language API are further options to put through the same five criteria, including where each processes the data.

The concession is real. If the job is research: exploring literature, or asking open questions of medical text, OpenBioLLM-70B and its 8B sibling are designed for that open-ended generation, and a 3B extractor was never built to converse. If the job is turning a stream of lab reports and discharge summaries into records a system can trust, without the data leaving the building, the small model is the one to test first. Our enterprise AI consulting guide covers how the DATS five-stage system moves a pilot to production.

Is OpenBioLLM-8B a fairer comparison than the 70B model?

OpenBioLLM-8B is closer in size to Mira-Q2 and easier to run, so it is a fairer hardware comparison than the 70B model. Its developer-reported average across the same nine datasets is 72.502, against 86.06 for the 70B model. It is still a general biomedical model scored on answer accuracy, so it needs the same extraction head-to-head before any conclusion.

Can Mira-Q2 or OpenBioLLM make clinical decisions on their own?

Neither Mira-Q2 nor OpenBioLLM should make clinical decisions on its own. OpenBioLLM's developer advises against use for direct patient care or clinical decision support, and every Mira-Q2 output is a draft for human review. Dilr Mira is an extraction tool whose outputs are never autonomous clinical decisions, and the team deploying either model keeps both the review step and the regulatory assessment.

Want to test this on your own documents? Try Mira-Q2 on Hugging Face, book a scoping call, explore our DATS methodology, or read more AI strategy guides on placing models inside regulated systems. If you are new to the line, start with what Dilr Mira is, or see how the same method applies to KYC onboarding documents.

Written by the Dilr.ai engineering team, practitioners who ship enterprise AI in production. Follow us on LinkedIn for shipping notes, or subscribe via the RSS feed.