Inkling vs GLM 5.2: Which Open-Weight Model Wins on Score, and Which Wins on Cost?
Inkling vs GLM 5.2 is one of the more revealing match-ups in the current wave of open-weight releases, because the two models optimize for different things. GLM 5.2, from Zhipu AI, is the agentic-terminal and reasoning leader in this comparison set — it posts the strongest scores on the hardest reasoning and long-horizon coding tasks. Inkling, the first model from Mira Murati’s Thinking Machines Lab, counters with dramatically better token efficiency, adversarial robustness, native audio and multimodal input, a 1-million-token context window, and an Apache 2.0 license. This article compares both models honestly, and it makes the case that raw benchmark gaps do not always translate into higher real-world cost.
As of: 2026-07-16, one day after Inkling’s launch. All figures are sourced and attributed below; none are independently audited.
A note for builders: there are no audited head-to-head benchmarks here, so this compares models and access, not scores. OrcaRouter routes API-available models behind a single OpenAI-compatible endpoint, so you can trial and compare Inkling and GLM 5.2 without wiring up multiple SDKs.
TL;DR verdict: Pick GLM 5.2 if you want the highest raw scores on reasoning, math, and agentic terminal work, and your budget can absorb its higher token consumption. Pick Inkling if cost per completed task, adversarial safety, audio/multimodal input, or a 1M-token context matter more than topping the leaderboard.
The one-liner: GLM 5.2 wins most benchmark rows; Inkling can still win the invoice, because it finishes tasks in roughly 25K output tokens versus GLM’s ~43K.
Key takeaways
GLM 5.2 leads the reasoning/agentic rows: HLE, AIME 2026, SWE-bench Verified, and — by a wide margin — Terminal Bench 2.1.
Inkling leads on adversarial safety: FORTRESS 78.0% vs 71.3%.
Inkling’s headline counter is efficiency: ~25K output tokens per task vs GLM’s ~43K — roughly a 1.7x difference that flows straight into cost.
Both are open-weight: Inkling is Apache 2.0; GLM 5.2 is MIT. Both allow commercial use and self-hosting.
Inkling adds modality and context: native text + image + audio input and up to a 1M-token context window.
Caveat: competitor numbers here are third-party/vendor-framed and not independently audited.
Disclosure: Benchmarks are vendor self-reported at launch (Effort 0.99) and third-party figures are from Artificial Analysis / MarkTechPost / Vellum / BenchLM; none are independently audited, and competitor numbers may differ from those vendors’ own reported figures. Inkling’s own specs are from Thinking Machines’ model card.
Quick-glance comparison
License. Inkling: Apache 2.0; GLM 5.2 (Zhipu AI): MIT
Parameters (total / active). Inkling: 975B / 41B (MoE); GLM 5.2 (Zhipu AI): — (not in our data)
Context window. Inkling: 1M tokens (256K on hosted APIs); GLM 5.2 (Zhipu AI): — (not in our data)
Modalities (in). Inkling: Text + image + audio; GLM 5.2 (Zhipu AI): — (not in our data)
Output. Inkling: Text only; GLM 5.2 (Zhipu AI): Text
Self-host / fine-tune. Inkling: Yes / yes (Tinker); GLM 5.2 (Zhipu AI): Yes (weights available) / yes
Hosted price. Inkling: ~$1.87 in / ~$4.68 out per 1M; GLM 5.2 (Zhipu AI): — (not in our data)
We do not have audited parameter, context, or pricing figures for GLM 5.2 in our source set, so those cells are marked “—” rather than guessed.
Winner by category
Reasoning / knowledge (HLE). Winner: GLM 5.2; Notes: 40.1% vs 29.7% (no tools)
Math (AIME 2026). Winner: GLM 5.2; Notes: 99.2% vs 97.1% — both near ceiling
Coding (SWE-bench Verified). Winner: GLM 5.2; Notes: 80.0% vs 77.6%
Agentic terminal (Terminal Bench 2.1). Winner: GLM 5.2; Notes: 82.7 vs 63.8 — the headline gap
Safety (FORTRESS adversarial). Winner: Inkling; Notes: 78.0% vs 71.3%
Multimodal / audio. Winner: Inkling; Notes: Native audio + image input
Efficiency (tokens/task). Winner: Inkling; Notes: ~25K vs ~43K
Cost per completed task. Winner: Inkling; Notes: Lower token use offsets per-token price
Head-to-head benchmarks
The table below uses one consistent source set (MarkTechPost) so the rows are comparable. Bold marks the leader.
HLE (no tools). Inkling: 29.7%; GLM 5.2: 40.1%; Source: MarkTechPost
AIME 2026. Inkling: 97.1%; GLM 5.2: 99.2%; Source: MarkTechPost
SWE-bench Verified. Inkling: 77.6%; GLM 5.2: 80.0%; Source: MarkTechPost
Terminal Bench 2.1. Inkling: 63.8%; GLM 5.2: 82.7%; Source: MarkTechPost
FORTRESS (adversarial). Inkling: 78.0%; GLM 5.2: 71.3%; Source: MarkTechPost
Two additional “quiet win” rows come from other sources and should not be blended with the MarkTechPost set above:
Token efficiency (output tokens/task, lower is better). Inkling: ~25K; GLM 5.2: ~43K; Source: Artificial Analysis / BenchLM
SWE-bench Pro (Public). Inkling: 54.3%; GLM 5.2: 62.1%; Source: Artificial Analysis / BenchLM
HLE with tools (kept separate from no-tools row). Inkling: 46.0; GLM 5.2: 54.7; Source: Vellum
Note: the “HLE with tools” figures come from Vellum and use a different harness than the MarkTechPost no-tools HLE row — do not read them as the same test. We do not have an Artificial Analysis Intelligence Index score for GLM 5.2 in our data, so we do not report one.
Where GLM 5.2 wins
GLM 5.2 is, on the numbers we have, the stronger raw reasoning and agentic model. It leads Inkling on HLE (40.1% vs 29.7%), AIME 2026 (99.2% vs 97.1%), and SWE-bench Verified (80.0% vs 77.6%). The most striking gap is Terminal Bench 2.1, where GLM 5.2 scores 82.7 to Inkling’s 63.8 — a large, real advantage on long-horizon agentic terminal tasks where a model has to plan, run commands, and recover from errors across many steps. On SWE-bench Pro, GLM 5.2 (62.1%) again pulls ahead of Inkling (54.3%), and it also leads the tool-augmented HLE with tools run (54.7 vs 46.0).
If your workload is dominated by hard reasoning, competition math, or agents that operate a shell or IDE over long sessions, GLM 5.2 is the higher-ceiling choice, and the gap is wide enough on the agentic rows to matter in production.
Where Inkling wins
Inkling’s counter is not a single benchmark — it is the economics and the surface area.
Token efficiency. Inkling completes tasks in roughly 25K output tokens versus GLM’s ~43K. Because you pay per output token, that ~1.7x difference is a direct cost lever. A model that scores a few points lower but uses far fewer tokens can be cheaper per completed task even at the same per-token price — and often finishes faster too.
Adversarial robustness. On FORTRESS, Inkling leads 78.0% to 71.3%. For adversarial or safety-sensitive deployments, that is the row that matters most.
Multimodality. Inkling accepts text, image, and audio input natively (VoiceBench 91.4%, MMAU 77.2% on its own card). GLM 5.2 in our data is a text-oriented model.
Context window. Inkling’s weights support up to 1M tokens (256K on hosted APIs) — useful for whole-repo, long-document, or long-transcript work.
Licensing. Both are permissive, but Inkling’s Apache 2.0 is a familiar, patent-clause-inclusive choice for enterprises; GLM 5.2 uses MIT. Either is fine for commercial self-hosting.
Pricing and cost (TCO)
The core insight of the Inkling vs GLM 5.2 comparison is that benchmark leadership and cost leadership are not the same thing.
Inkling’s weights are royalty-free to self-host under Apache 2.0. Third-party hosted access (via Artificial Analysis’ reference pricing) runs about $1.87 per 1M input tokens and $4.68 per 1M output tokens at 64K context (roughly $3.74 / $9.36 at 256K), with cached input near $0.374 per 1M. We do not have published hosted pricing for GLM 5.2 in our source set, so we compare on structure rather than a fabricated number.
Here is why the cost-per-task angle matters. Suppose a task needs the same per-token rate on both models. Inkling burns ~25K output tokens; GLM 5.2 burns ~43K. That means GLM 5.2 costs roughly 72% more in output tokens for the same job, before you even account for latency. So even though GLM 5.2 wins most benchmark rows, an organization running high volumes of routine tasks may find Inkling delivers a lower total cost of ownership — the efficiency advantage can offset a modest raw-score gap. The honest rule: use GLM 5.2 where the extra reasoning headroom is worth the extra tokens; use Inkling where volume and cost dominate.
Licensing and deployment
Both models are genuinely open-weight and self-hostable:
Inkling — Apache 2.0. Full BF16 and NVFP4 checkpoints on Hugging Face. VRAM tiers: BF16 ~2TB (8×B300 / 16×H200); NVFP4 ~600GB (4×B300 / 8×H200); an Unsloth 1-bit GGUF exists for constrained setups. Hosted on Together AI, Fireworks, Modal, Databricks, and Baseten; runs on SGLang, vLLM, TokenSpeed, Unsloth, and Hugging Face transformers. Fine-tuning via Tinker (64K/256K context, 50% launch discount).
GLM 5.2 — MIT. Open weights are available for commercial use and self-hosting under the permissive MIT license. Specific VRAM and provider details are not in our source set, so check Zhipu AI’s release for exact requirements.
Quickstart for Inkling with vLLM:
vllm serve thinkingmachines/Inkling --tensor-parallel-size 8
Which should you choose?
Choose GLM 5.2 if: you want the strongest raw reasoning and math, or you build long-horizon terminal/agentic workflows where its Terminal Bench 2.1 and SWE-bench Pro leads pay off. It is the higher-ceiling model in this pair.
Choose Inkling if: you run high volumes and care about cost per completed task, need adversarial robustness (FORTRESS), require audio or image input, or need a 1M-token context. Its efficiency advantage is the reason to look past a few benchmark points.
Consider running both: route hard reasoning and complex agent runs to GLM 5.2, and send high-volume, cost-sensitive, or multimodal traffic to Inkling. A two-model router captures GLM’s ceiling and Inkling’s efficiency at the same time.
For a deeper look at Inkling itself, see our Inkling AI model review and the What is Inkling AI? explainer. For other head-to-head matchups, see Inkling vs Kimi K2.6 and Inkling vs DeepSeek V4 Pro.
FAQ
Is Inkling better than GLM 5.2? It depends on the metric. GLM 5.2 wins most raw benchmark rows in this set — HLE, AIME 2026, SWE-bench Verified, and especially Terminal Bench 2.1. Inkling wins on adversarial safety (FORTRESS), token efficiency, multimodality, and context length. Inkling can be “better” on cost per completed task even where it scores lower.
Which is better for coding? GLM 5.2 leads on both SWE-bench Verified (80.0% vs 77.6%) and SWE-bench Pro (62.1% vs 54.3%), and its Terminal Bench 2.1 lead (82.7 vs 63.8) is significant for agentic, multi-step coding. For raw coding capability, GLM 5.2 is ahead; for cost-efficient coding at volume, Inkling’s token efficiency narrows the gap.
Which is cheaper? Inkling is likely cheaper per completed task. It uses roughly 25K output tokens per task versus GLM’s ~43K, so even at similar per-token rates it consumes far fewer billable tokens. Both are royalty-free to self-host (Apache 2.0 for Inkling, MIT for GLM 5.2).
Is GLM 5.2 open source? GLM 5.2 is open-weight under the MIT license, which permits commercial use and self-hosting. As with all “open-weight” models, weights and license are released, but that is not identical to full open-source (training data and pipeline are not necessarily published).
Can I self-host or fine-tune GLM 5.2? Yes. GLM 5.2’s MIT-licensed weights can be self-hosted and fine-tuned. Inkling can likewise be self-hosted (Apache 2.0) and fine-tuned via Thinking Machines’ Tinker platform. Specific GLM 5.2 hardware requirements are not in our source set — check Zhipu AI’s release.
Does GLM 5.2 support audio or images? Our source set does not list audio or image input support for GLM 5.2, so we treat it as text-oriented here. Inkling natively accepts text, image, and audio input, which is one of its clearest advantages in this comparison.
Conclusion
GLM 5.2 is the raw-capability leader in this matchup, topping Inkling on reasoning, math, and — most decisively — agentic terminal work. But Inkling answers with roughly 1.7x better token efficiency, stronger adversarial safety, native multimodality, a 1M-token context, and an Apache 2.0 license. The practical takeaway: pick GLM 5.2 when the reasoning ceiling justifies the extra tokens, pick Inkling when cost per completed task and multimodality matter, and consider routing between them to get the best of both.
