BALROG
BALROG is an open ICLR-published benchmark that scores agentic LLM and VLM reasoning across seven procedurally generated games.
If you are picking a model for an agentic, planning-heavy workload, BALROG is the most honest public signal available — it forces sustained multi-step decisions instead of trivia recall. GPT-6-Astra-Max's 68.3%, Claude-Opus-5-Max's 63.4% and GPT-5.6-Sol-Max's 60.0% sit at the top of the LLM board, and yet no model clears 14% on NetHack. That gap is the point: strong game scores still do not prove reliability on your job. We would treat BALROG as one input alongside task-specific evals, not as a verdict on its own.
Verified 2d ago · liveness 60/100 · cite: rightaichoice.com/tools/balrog
- AI researchers benchmarking agentic reasoning and long-horizon planning in LLMs and VLMs
- Model developers testing spatial reasoning and sustained multi-step decisions
- Academic labs comparing frontier and open-weight generalisation on the same harness
- Teams deciding between models for a planning-heavy autonomous agent workload
- Teams wanting a hosted evaluation API you point at a model endpoint — there is none
- Non-technical buyers: submission requires a GitHub repository and custom wrapper code
- Quick knowledge-recall benchmarking — MMLU or similar is the right tool
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Skip BALROG if you need a hosted evaluation API, a managed regression dashboard, or a non-technical way to score a model — submission requires writing a wrapper and pushing a GitHub repository.
Running the seven environments yourself means paying for your own model inference, and NetHack runs are long — budget tokens before you commit to a full sweep
BALROG itself is open research infrastructure — the leaderboards, evaluation code and submission path are published, with the ICLR paper and methodology alongside them. The real cost is your own model inference and engineering time to write a wrapper. For a planning-heavy agent decision, that cost is small next to the price of picking the wrong model on a marketing claim.
In short
BALROG — BALROG is an open ICLR-published benchmark that scores agentic LLM and VLM reasoning across seven procedurally generated games. Best for AI researchers benchmarking agentic reasoning and long-horizon planning in LLMs and VLMs, Model developers testing spatial reasoning and sustained multi-step decisions, Academic labs comparing frontier and open-weight generalisation on the same harness. Free to use.
What's new in BALROG
Checked todayAcross the latest 4 updates: 4 changelog entries.
Gemini-3.1-Pro-Thinking tops LLM leaderboard
Gemini-3.1-Pro-Thinking reaches 57.0 ± 2.0 average progress across the seven environments, the highest score on the BALROG LLM leaderboard as of February 2026.
Claude-Opus-4.5 and Claude-Haiku-4.5 added to leaderboard
Claude-Opus-4.5 scores 43.5 ± 2.3 and Claude-Haiku-4.5 scores 31.2 ± 2.1 average progress on the LLM leaderboard.
Gemini-3-Flash tops VLM leaderboard
Gemini-3-Flash reaches 48.1 ± 2.4 average progress, the highest score on the BALROG vision-language leaderboard.
Gemini-3-Pro achieves 58.1% average progress
Gemini-3-Pro records 58.1 ± 2.1 average progress on the LLM leaderboard, with 96.0 on BabyAI and 6.8 on NetHack.
What people actually say about BALROG — is it worth it?
We scanned public community sources for BALROG on Jul 3, 2026 and could not establish that the discussion we found is about this tool rather than something else sharing its name. Our own analysis of that scan says the posts were off-subject. Rather than publish a sentiment score built on the wrong subject, we publish nothing here and re-run the scan.
Viability Score
How well maintained and how widely used is BALROG? Built from what the vendor actually publishes (docs, changelog, tutorials, integrations, pricing), whether the site is live, and how much real users discuss it. How we calculate this
Last calculated: October 2026
How we score →Key Features
- Public LLM leaderboard for language-only agentic evaluation
- Separate public VLM leaderboard for vision-language evaluation
- Seven procedurally generated game environments: BabyAI, Crafter, TextWorld, BabaIsAI, MiniHack, NetHack
- Per-game completion-percentage breakdown for every model entry
- Average % Progress metric with confidence intervals across all environments
- Procedural level generation to block memorisation
- Weekly leaderboard refresh, every Monday
- Submission via GitHub repository
- Open-source evaluation and submission code
- Checkmark column marking models reproduced by the BALROG team with direct system access
- Gamepad icon marking submissions with open-source code
- Mode Analysis view contrasting LLM and VLM behaviour
- ICLR-published benchmark methodology (OpenReview fp6t3F669F)
- Interactive long-horizon tasks rather than single-question grading
About BALROG
BALROG (Benchmarking Agentic LLM/VLM Reasoning On Games) is an open research benchmark that measures how well large language and vision-language models plan, explore and remember across seven procedurally generated environments, including BabyAI, Crafter, TextWorld, BabaIsAI, MiniHack and NetHack. Rather than grading a single answer, it runs models interactively through long-horizon tasks and reports an average completion percentage per environment, with the leaderboard refreshed weekly every Monday. The benchmark splits into two public leaderboards — one for language-only LLM evaluation and one for vision-language VLM evaluation. Procedural level generation stops models from memorising solutions, which turns the scores into a generalisation test rather than a recall test. Per-game breakdowns make the shape of failure legible: models clear BabyAI and Crafter, then collapse on NetHack. On the current LLM board, GPT-6-Astra-Max leads at 68.3% (±2.0), with Claude-Opus-5-Max at 63.4% and GPT-5.6-Sol-Max at 60.0%. Gemini-3-Pro sits at 58.1% and Gemini-3.1-Pro-Thinking at 57.0%. On the VLM board, Gemini-3-Flash tops at 48.1%. The depth of the remaining gap is visible in the per-game columns: even the leading models score in single digits on NetHack, with GPT-6-Astra-Max the only entry above 13% there. It is evaluation infrastructure, not a hosted inference API. You bring your own model access, write a wrapper, and submit a repository. A checkmark column marks models the BALROG team reproduced with direct system access, and a gamepad icon flags open-source submissions.
Behind the Verdict
BALROG's value is that it is hard to game. Seven procedurally generated games mean a model cannot memorise a solution path; it has to plan, explore and hold context across many steps. That is a genuinely different measurement from MMLU (knowledge recall) or SWE-bench (code repair), and the per-game breakdown is where the real reading happens — a model that scores 100% on BabyAI and 2% on NetHack is telling you something specific about its long-horizon spatial reasoning. Strengths: open-source evaluation and submission code, a published ICLR paper, a reproducibility column marking models the team re-ran with direct system access, and weekly Monday refreshes. The split LLM/VLM leaderboards let you compare a model against its own text-only or vision-only counterpart on identical environments. Recent submissions are dense — Claude-Opus-5-Max at 63.4% (2026-09-20), GPT-6-Astra-Max at 68.3% (2026-09-18), Gemini-3.1-Pro-Thinking at 57.0% (2026-02-25) — so the board tracks the frontier rather than lagging it. Weaknesses: this is research infrastructure, not a product. Scoring means running the games yourself and submitting via GitHub; there is no managed dashboard or support contract. Scope is narrow and interactive, so strong BALROG scores do not transfer automatically to coding, support or document workflows. Aggregate averages hide variance, which is why the confidence intervals (±) and per-game columns matter more than the headline number — a 68.3 ± 2.0 average tells you far less than the fact that the same model scores 13.2 ± 2.7 on NetHack. Where it fits: research labs, model developers and infrastructure teams choosing a model for multi-step autonomous agent work, and anyone who needs a memorisation-resistant interactive eval rather than a static QA set. Where it doesn't: non-technical buyers, teams wanting a hosted evaluation API, and anyone testing production agent behaviour outside these seven games.
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Real-world workflow fit
Concrete scenarios for the personas BALROG actually fits — and what changes day-one when you adopt it.
You shortlist two frontier models and a 70B open-weight candidate. You clone the BALROG evaluation code, wrap each model endpoint, and run all seven procedurally generated games, then compare average % progress and the per-game columns before submitting.
Outcome: You see that the top model leads on BabyAI and Crafter but only scores single digits on NetHack, so you plan for human-in-the-loop fallbacks on spatial exploration tasks rather than trusting the headline average.
You submit your model through the GitHub repository with open-source code so the gamepad icon marks it, and check the checkmark column to see whether the BALROG team reproduced comparable entries with direct system access.
Outcome: Your result lands on a standardised, weekly-refreshed public leaderboard next to frontier entries, with per-game breakdowns and published confidence intervals instead of a self-reported number.
You run your vision-language model on the VLM board and the text-only variant on the LLM board, then open the Mode Analysis view to compare how the two modes behave on the same environments.
Outcome: You isolate whether the gap comes from visual grounding or from long-horizon planning — a distinction the aggregate average alone would have hidden.
Use Cases
- Benchmark a model's planning and memory over long-horizon interactive tasks rather than single questions
- Compare a vision-language model against its text-only counterpart on the same environments
- Localise failure by game — see whether a model breaks on NetHack exploration versus BabyAI instruction following
- Track open-weight models such as Llama-3.3-70B-it or Qwen2.5-72B-it against frontier proprietary models
- Publish reproducible agentic reasoning results on a standardised leaderboard
- Check whether a frontier model's advertised agentic ability holds up under procedurally generated levels
- Read the Mode Analysis view to see how LLM and VLM evaluation modes diverge on identical tasks
Models Under the Hood
as of 2026-10-16
Limitations
- BALROG is an ICLR-published research benchmark rather than a product: scoring is done by running the seven procedurally generated games yourself and submitting results through GitHub, so there is no managed dashboard or support contract.
- Its scope is deliberately narrow and interactive — BabyAI, Crafter, TextWorld, BabaIsAI, MiniHack, NetHack and one further environment — so strong game scores do not automatically translate into reliability on coding, support or document workflows.
- Aggregate averages hide variance, which is why the per-game breakdowns and the reported ± confidence intervals matter more than the headline number: GPT-6-Astra-Max leads at 68.3 ± 2.0 overall while still scoring 13.2 ± 2.7 on NetHack, and Claude-Opus-5-Max reaches 100.0 ± 0.0 on BabyAI but only 7.3 ± 1.6 on NetHack.
as of 2026-10-09
Verification history
We have re-verified BALROG 8 times since . Each pass re-reads the vendor's own pages and re-checks every listed field against that evidence; passes where nothing had changed are marked as such.
- — re-checked, vendor evidence unchanged
- — re-verified summary, description, our verdict, our analysis, pricing model, pricing tiers, features, integrations, who it suits, who should skip it
- — re-verified summary, description, our verdict, our analysis, pricing model, pricing tiers, features, integrations, who it suits, who should skip it
- — re-verified summary, description, our verdict, our analysis, pricing model, pricing tiers, features, integrations, who it suits, who should skip it
- — re-checked, vendor evidence unchanged
- — re-verified summary, description, our verdict, our analysis, pricing model, pricing tiers, features, integrations, who it suits, who should skip it
Showing the 6 most recent of 8 verification passes.
Free to cite with attribution — this page re-verifies continuously.
12-month cost
Project the real annual outlay, including the implied monthly cost when only an annual tier is published.
Vendor list price only. Add-on usage, seat overages, and contract minimums are surfaced under Hidden costs & gotchas.
Plans compared
For each published BALROG tier: who it actually fits, and what it adds vs. the previous tier. Cross-reference the cost calculator above for projected annual outlay.
Open source
$0
Ideal for
AI researchers, model developers and academic labs with Python and GitHub capability who can run the seven environments themselves and submit results
What this tier adds
Starting tier — the only tier: free access to both leaderboards, evaluation and submission code, the ICLR paper, and weekly Monday refreshes
Where the pricing makes sense
The company stage and team size where BALROG's pricing actually pencils out — and where peers do it cheaper.
BALROG itself is open research infrastructure — the leaderboards, evaluation code and submission path are published, with the ICLR paper and methodology alongside them. The real cost is your own model inference and engineering time to write a wrapper. For a planning-heavy agent decision, that cost is small next to the price of picking the wrong model on a marketing claim.
Setup time & first value
How long it actually takes to get something useful out of BALROG — broken out by persona, not the marketing-page minute.
For a researcher already comfortable with evaluation harnesses: a few hours to clone the code, write a wrapper around your model endpoint and get one environment running, then longer for a full seven-game sweep because NetHack runs are long. For a non-technical buyer, this is not a same-day setup — you need Python and GitHub workflow familiarity before you start.
Switching to or from BALROG
How to bring data in from common predecessors and how to get it back out — written for the switcher, not the buyer.
- →From static QA benchmarks such as MMLU: keep your knowledge-recall scores and add BALROG when you need interactive, multi-step planning evidence
- →From self-reported agent demos: replace the demo video with a procedurally generated, memorisation-resistant score on the seven games
- ↗To SWE-bench: if your workload is code repair rather than long-horizon planning, a task-specific eval measures the thing you actually ship
- ↗To a task-specific eval suite: build your own environment harness when your agent's real world is support tickets or document workflows rather than games
Resources & Guides
Tutorials & Learning
YouTube returned 6 videos for “BALROG”, and we withheld 6: 6 could not be judged, because “BALROG” is a single word that other videos use for other things. We are showing none, because we could not prove any of them are about BALROG.
Official links
Tools that pair well with BALROG
Common stack mates teams adopt alongside BALROG, with the specific reason each pairing earns its keep.
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Featured Head-to-Head Comparisons
Balrog vs Surge Ai
If you need a free, open-source benchmark to compare LLM/VLM reasoning on interactive tasks, BALROG is ideal. However, for frontier labs training or red-teaming advanced models with expert human feedback, Surge AI's curated workforce and specialized benchmarks (like Riemann-bench, ComplexConstraints) deliver far deeper insights—as shown by its use by Microsoft and its recent benchmark releases.
Balrog vs Praktika
BALROG and Praktika serve entirely different needs — BALROG is a research benchmark for evaluating LLM/VLM agentic reasoning via games, while Praktika is a mobile app for AI-driven language conversation practice. If you're an AI researcher needing a rigorous, open-source evaluation platform, choose BALROG. If you're an intermediate language learner wanting real-time speaking practice with AI tutors, Praktika is the better fit.
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Frequently Asked Questions
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