> This is the markdown version of https://www.maniac.ai/blog/minimax-m2-7-vs-glm-5-1-vals-benchmarks. Visit the full page for interactive content. # MiniMax M2.7 vs GLM 5.1 for long-horizon agents | Arendil | Arendil [Blog](/blog) April 12, 2026 If you care about **long-horizon agent work**, the important question is not who looks best on a giant mixed leaderboard. It is who looks stronger on tasks that resemble what agents actually do: **multi-step coding, tool use, repository repair, and workflows that can go wrong halfway through**. The most relevant rows are: - **Finance Agent (v1.1)** - **Terminal-Bench 2.0** - **SWE-bench** - **LiveCodeBench** - **Vibe Code Bench** I also use **latency**, **token pricing**, **context window**, and **max output tokens** because those operational constraints compound quickly once an agent starts taking multiple turns. * * * ## TL;DR - **GLM 5.1** wins all five of the long-horizon rows I would care about first: **Finance Agent**, **Terminal-Bench 2.0**, **SWE-bench**, **LiveCodeBench**, and **Vibe Code Bench**. - The biggest gaps are **Finance Agent (57.66% vs 48.40%)** and **Terminal-Bench 2.0 (53.93% vs 47.19%)**. - A simple average across those five rows gives **GLM 5.1 at 60.17%** versus **MiniMax M2.7 at 55.27%**. That average is my own rollup from the cited public numbers, not an official benchmark metric. - **MiniMax M2.7** is still much cheaper: **$0.30 / $1.20** input/output versus **$1.00 / $3.20** for GLM 5.1. - For benchmarked long-horizon performance, **GLM looks stronger today**. For cost-constrained long runs, **MiniMax is the budget play**. * * * ## The long-horizon rows Benchmark GLM 5.1 MiniMax M2.7 Edge **Finance Agent (v1.1)** **57.66%** **48.40%** **GLM +9.26** **Terminal-Bench 2.0** **53.93%** **47.19%** **GLM +6.74** **SWE-bench** **76.40%** **73.80%** **GLM +2.60** **LiveCodeBench** **81.38%** **79.93%** **GLM +1.45** **Vibe Code Bench** **31.46%** **27.04%** **GLM +4.42** **Simple average of rows above** **60.17%** **55.27%** **GLM +4.89** This is the cleanest summary I can get if the question is "which model looks better for agentic work that unfolds over multiple steps?" On every row that looks most relevant to that question, **GLM is ahead**. * * * ## Why these rows matter more than the overall index The overall **Index** still favors **GLM 5.1**, **63.17%** to **59.58%**, and the overall **coding** category average does too, **60.79%** to **56.99%**. That is directionally useful. But if your use case is specifically **long-horizon work**, the overall index is not the main thing to watch because it mixes in categories that are much less agent-like. A strong **CaseLaw** or **GPQA** row can matter for some buyers, but it does not tell you nearly as much about a model that has to: - plan across multiple turns - recover after a bad step - use tools or act through a runtime - modify code or reason over a repository That is why the **Finance Agent**, **Terminal-Bench**, **SWE-bench**, and broader coding rows are the real signal here. * * * ## Where GLM creates the clearest separation The most important thing here is not that GLM wins by a little. It is that the rows most associated with multi-step agent loops all point in the same direction. Two rows stand out most: - **Finance Agent (v1.1): 57.66% vs 48.40%** - **Terminal-Bench 2.0: 53.93% vs 47.19%** Those are the biggest gaps among the long-horizon rows, and they are also the rows that most obviously suggest **agents acting through tools or extended workflows** rather than pure one-shot completion. The coding rows reinforce the same story: - **SWE-bench:** 76.40% vs 73.80% - **LiveCodeBench:** 81.38% vs 79.93% - **Vibe Code Bench:** 31.46% vs 27.04% None of those individual coding gaps are gigantic on their own. The point is that **they all stack in the same direction**. If you care about whether an agent can keep a repo task alive across multiple steps, the public benchmark picture is more favorable to **GLM 5.1**. * * * ## What MiniMax buys you anyway MiniMax's case is not benchmark leadership on these rows. Its case is **economics** and a couple of operational constraints that can matter once long runs get expensive. Operational metric GLM 5.1 MiniMax M2.7 Read **Input cost** **$1.00 / 1M** **$0.30 / 1M** MiniMax is **70% cheaper** **Output cost** **$3.20 / 1M** **$1.20 / 1M** MiniMax is **62.5% cheaper** **Latency** **7.63m** **10.33m** GLM is lower on Vals' latency row **Context window** **200K** **197K** Essentially tied **Max output tokens** **131K** **197K** MiniMax allows longer output On a workload with **1M input tokens and 1M output tokens**, the cited public prices imply: - **GLM 5.1:** **$4.20** - **MiniMax M2.7:** **$1.50** That makes MiniMax about **64% cheaper** on a balanced input/output workload. If your agent loops are long, branchy, or retry-heavy, that cost gap can buy you a lot of extra attempts. So even though **GLM looks better on the long-horizon benchmark rows**, **MiniMax is still economically interesting** if you are willing to trade some benchmark headroom for much cheaper inference. * * * ## Do not over-read the close coding rows The cited benchmark rows also include `+/-` ranges beside the point estimates. Some of the coding rows are close enough that I would not call them decisive from this page alone: - **LiveCodeBench:** 81.38% +/- 1.06 for GLM vs 79.93% +/- 1.10 for MiniMax - **SWE-bench:** 76.40% +/- 1.90 for GLM vs 73.80% +/- 1.97 for MiniMax That is why I would not summarize this as "GLM crushes MiniMax." The more honest read is: - **GLM is ahead on every long-horizon row selected here** - **The strongest practical gaps are on Finance Agent and Terminal-Bench** - **MiniMax is much cheaper, which may matter more than a few benchmark points in production** - **Some coding deltas are real but not huge, so your own workload still matters** * * * ## What to choose If you optimize for the **best public benchmark picture on long-horizon tasks**, **GLM 5.1** is the cleaner pick today. It leads on the rows that look most like tool use, multi-step coding, and agentic workflows, and it also has the lower latency row. If you optimize for **cost per token** and expect long runs to dominate your bill, **MiniMax M2.7** is still very much alive in the conversation. A model that is roughly **64% cheaper** on balanced token spend, with a slightly longer max output allowance, can still be the right economic choice even if the benchmark rows are weaker. That is the real takeaway: **GLM buys you more benchmark headroom on long-horizon work, while MiniMax buys you more room to spend on long-horizon inference**. * * * ## Source - Benchmark rows, latency, context, max output, and token pricing cited from [Vals' GLM 5.1 vs MiniMax M2.7 comparison](https://www.vals.ai/comparison?modelA=zai%2Fglm-5.1-thinking&modelB=minimax%2FMiniMax-M2.7) --- *Arendil, High throughput background agents. Opus-quality outputs at 1/50 of the cost. Learn more at [maniac.ai](https://www.maniac.ai).*