GLM-5.1 API Pricing Per Million Tokens 2026: Cost Guide & LLM Comparison

TL;DR — GLM-5.1 API pricing in 2026 is $1.40 per million input tokens and $4.40 per million output tokens from Z.ai (the company formerly known as Zhipu AI). That puts GLM-5.1 in the mid-tier: roughly 3.5x cheaper than Claude Opus 4.8 or GPT-5.5 on input, but pricier than DeepSeek V4 and Kimi K2.6. GLM-5.1 self-reports 58.4% on SWE-Bench Pro — a number it claims edges out GPT-5.4 and Claude Opus 4.6 — and its weights are open-sourced under MIT, so you can self-host. The catch: that benchmark is provider-reported and not yet independently corroborated, and GLM-5.1's 64K context is smaller than its rivals'. Teams using ClawRouters to auto-route each request to the cheapest model that can actually handle it cut their total LLM bill 40-60% without betting everything on a single provider's self-reported scores.

If you've been searching for glm-5.1 api pricing per million tokens 2026, this is the definitive breakdown. We cover GLM-5.1's exact per-token cost, how it compares against every major Western and Chinese provider, real monthly cost scenarios, the open-weights angle most pricing pages ignore, and how to cut your bill further. For the broader market, see our full LLM API pricing guide for 2026.

GLM-5.1 API Pricing Table (June 2026)

All prices are per million tokens (MTok). These reflect Z.ai's published platform rates.

| Model | Input (/1M) | Output (/1M) | Context | Best For | |-------|------------|-------------|---------|----------| | GLM-5.1 | $1.40 | $4.40 | 64K | Coding, complex reasoning, Chinese-language tasks | | GLM-4 Plus (legacy) | $0.50 | $1.50 | 128K | Simple Q&A, back-compat, low-complexity Chinese |

Prices as of June 2026, per 1 million tokens. GLM-5.1 released 2026-03-27; weights open-sourced 2026-04-07 under MIT.

What makes GLM-5.1 pricing different

Three things set glm-5.1 api pricing apart from the rest of the market:

• Open weights under MIT. Z.ai open-sourced GLM-5.1's weights on 2026-04-07 under a permissive MIT license. The model was trained on Huawei Ascend chips. If you have the GPUs, you can self-host and pay zero per-token — but for most teams the hosted API is cheaper than running the hardware, especially at low-to-moderate volume.
• A balanced input-to-output ratio. GLM-5.1's output ($4.40) is about 3.1x its input ($1.40). That's a tighter ratio than Kimi K2.6's 6.7x or GPT-5.5's 6x, which means GLM-5.1 doesn't punish output-heavy workloads as harshly as some cheaper-on-input rivals.
• Mid-tier positioning. GLM-5.1 isn't trying to be the cheapest (DeepSeek and Gemini Flash win there) or the highest-quality (Claude Opus and GPT-5.5 lead on reliability). It targets the value middle: near-frontier coding and reasoning claims at a fraction of frontier prices.

GLM-5.1: What You're Paying For

GLM-5.1 is Z.ai's flagship, released 2026-03-27. The headline claim is performance, not just price:

• SWE-Bench Pro: 58.4% (self-reported). Z.ai claims this is #1, edging out GPT-5.4 (57.7%) and Claude Opus 4.6 (57.3%) on real-world software engineering tasks.
• Strong Chinese-language ability — GLM has always been a Chinese-language specialist, and 5.1 is the strongest in the line for bilingual and Chinese-first products.
• Tool use and JSON mode — built for agentic and structured-output workflows.
• 64K context window — adequate for most tasks, but notably smaller than Kimi K2.6 (256K), Gemini 3 (1M), or Claude (1M).

The honest caveat: that 58.4% SWE-Bench Pro figure is provider-reported and not yet independently corroborated. Self-reported benchmarks from any vendor deserve a healthy reliability discount until third parties confirm them on neutral eval sets. That's exactly why our routing engine scores GLM-5.1 conservatively (capability 4, not 5, across coding and reasoning) pending a month of production validation — more on that below.

Real-World Monthly GLM-5.1 API Cost Scenarios

Per-token numbers are abstract. Here's what the glm-5.1 api pricing translates to at real production volumes:

| Daily Volume | Monthly Cost | |-------------|-------------| | 500K in + 500K out | $87 | | 2M in + 2M out | $348 | | 5M in + 5M out | $870 | | 10M in + 2M out (read-heavy) | $684 | | 2M in + 10M out (write-heavy) | $1,404 |

Two patterns stand out:

1. The balanced ratio keeps write-heavy bills reasonable. The write-heavy row (2M in / 10M out) costs $1,404/month — high, but GLM-5.1's 3.1x output multiplier means it scales more gently than models with a 6x+ ratio. For verbose generation, GLM-5.1 is less punishing than Kimi K2.6.
2. There's no built-in cache discount to lean on. Unlike Moonshot's automatic 50% cache on input, GLM-5.1's published rates are flat. That makes per-request model selection — not caching tricks — your biggest lever for savings.

This is exactly the kind of tradeoff that makes single-provider lock-in expensive. The right model depends on your input/output mix, and that mix varies per request.

GLM-5.1 vs. Other LLM Providers (2026)

Here's how glm-5.1 api pricing stacks up against the major providers, sorted by input cost:

| Provider | Model | Input (/1M) | Output (/1M) | Context | Notes | |----------|-------|------------|-------------|---------|-------| | Google | Gemini 3 Flash | $0.075 | $0.30 | 1M | Cheapest overall, great for high-volume simple tasks | | DeepSeek | DeepSeek V4 Flash | $0.14 | $0.28 | 128K | Best output value, strong coding/math | | Zhipu / Z.ai | GLM-4 Plus (legacy) | $0.50 | $1.50 | 128K | Cheap Chinese-language baseline | | Moonshot | Kimi K2.6 | $0.60 | $4.00 | 256K | Near-frontier agentic coding, 256K context | | Google | Gemini 3 Pro | $1.25 | $5.00 | 1M | Long-context multimodal | | Zhipu / Z.ai | GLM-5.1 | $1.40 | $4.40 | 64K | Open-weight, coding + Chinese, mid-tier price | | DeepSeek | DeepSeek V4 Pro | $1.74 | $3.48 | 128K | Premium coding, 81% SWE-Bench Verified | | OpenAI | GPT-4o | $2.50 | $10.00 | 128K | General-purpose, vision | | Anthropic | Claude Sonnet 4.6 | $3.00 | $15.00 | 1M | Strong all-rounder | | OpenAI | GPT-5.5 | $5.00 | $30.00 | 256K | OpenAI flagship | | Anthropic | Claude Opus 4.8 | $5.00 | $25.00 | 1M | Best reasoning, agentic coding |

Prices as of June 2026, per 1 million tokens.

GLM-5.1 vs. DeepSeek: the closest fight

DeepSeek is GLM-5.1's most direct competitor — another Chinese, open-weight, coding-focused provider. On input, DeepSeek V4 Flash ($0.14) is 10x cheaper than GLM-5.1 ($1.40), and even DeepSeek V4 Pro ($1.74) is in the same neighborhood while posting an independently respected 81% SWE-Bench Verified. On output, DeepSeek V4 Flash ($0.28) is nearly 16x cheaper.

So why pick GLM-5.1? Mainly for Chinese-language strength and the open-weight MIT license, plus the SWE-Bench Pro claim if it holds up. But on raw, independently-verified cost-per-task for coding, DeepSeek currently has the stronger evidence. For pure cost-per-task on coding, see our cheapest AI API for coding guide.

GLM-5.1 vs. Kimi K2.6: the mid-tier Chinese rivals

Kimi K2.6 ($0.60/$4.00) undercuts GLM-5.1 ($1.40/$4.40) on both input and output, and ships a far larger 256K context window (vs. GLM-5.1's 64K). For long-horizon agentic coding over big codebases, Kimi's context advantage and lower input price usually win. GLM-5.1's edge is its tighter output ratio and its Chinese-language depth. If your prompts are short and Chinese-heavy, GLM-5.1 competes; if they're long-context agentic workloads, Kimi is typically the better value. See our Moonshot Kimi API pricing guide for that side.

GLM-5.1 vs. Claude & GPT: the value gap

Against the Western frontier, GLM-5.1's price advantage is real but smaller than the cheapest Chinese models. GLM-5.1 input ($1.40) is about 3.5x cheaper than Claude Opus 4.8 or GPT-5.5 ($5.00). On output, GLM-5.1 ($4.40) undercuts Opus 4.8 ($25.00) by 5.7x and GPT-5.5 ($30.00) by 6.8x.

The honest caveat: Claude and GPT still lead on instruction-following reliability and tool-call consistency in production — areas where GLM-5.1 scores lower in our internal capability matrix (instruction_following: 3 of 5). For mission-critical tool chains, a more expensive model sometimes pays for itself in fewer retries. That's why our router treats Chinese-provider instruction-following with a small reliability buffer rather than trusting raw benchmarks alone.

When GLM-5.1 Is the Right Choice

Based on the pricing structure and capability profile, GLM-5.1 is the cost-optimal pick when:

• You need Chinese-language strength. GLM-5.1 is a Chinese-language specialist (capability 5 of 5); for bilingual or Chinese-first products it outperforms most Western models per dollar.
• Your tasks are medium-to-high complexity coding or reasoning that don't require a huge context window. The 64K limit is fine for most single-file or focused multi-file work.
• You want open weights as a hedge. The MIT license means you can self-host if pricing or availability ever changes, avoiding hard lock-in.
• You want near-frontier quality claims without frontier prices — when DeepSeek feels too risky on a hard task but Opus is overkill on budget.

GLM-5.1 is the wrong choice when you need a large context window (Kimi K2.6 or Gemini 3 win), when raw cost is the only priority (DeepSeek V4 Flash or Gemini 3 Flash are far cheaper), or when you need maximum tool-call reliability for production agents — cases where Sonnet 4.6 or GPT-5.5 may be cheaper per successful completion.

How to Cut Your GLM-5.1 API Cost Further

Even at $1.40/$4.40, you can reduce your glm-5.1 api pricing bill:

1. Use GLM-4 Plus for the easy stuff. Simple Q&A and low-complexity Chinese tasks don't need 5.1. The legacy GLM-4 Plus ($0.50/$1.50) handles them at a third of the input price.
2. Don't send everything to GLM-5.1. Simple classification or extraction belongs on Gemini 3 Flash ($0.075/$0.30) or DeepSeek V4 Flash. Reserve GLM-5.1 for the coding, reasoning, and Chinese tasks where its quality justifies the price.
3. Watch your output tokens. Output is 3.1x your input cost. Trimming verbose system prompts and capping max_tokens on bounded tasks directly cuts the priciest part of the bill.
4. Consider self-hosting at scale. The open MIT weights make self-hosting viable if your volume is high enough to amortize GPU costs — though for most teams the hosted API stays cheaper.
5. Route by request, not by provider. The single biggest lever is matching each request to the cheapest model that can handle it — which no static configuration can do well, because complexity varies request to request.

Let ClawRouters Optimize GLM-5.1 Pricing Automatically

Here's the core problem with picking any single model — including GLM-5.1: the optimal model changes per request. A simple extraction call wastes money on GLM-5.1 when Gemini Flash would do. A hard agentic task with a large codebase underperforms on GLM-5.1's 64K context when Kimi's 256K would handle it. And a mission-critical tool chain may cost you in retries on GLM-5.1 when Sonnet 4.6 finishes first-try.

ClawRouters solves this by analyzing each incoming prompt and routing it to the optimal model across Z.ai, OpenAI, Anthropic, Google, DeepSeek, Moonshot, and other providers — based on task type, complexity, and your cost strategy. You keep an OpenAI-compatible API; you just change your base_url. GLM-5.1 is already in the routing pool, automatically selected for the Chinese-language and medium-complexity coding tasks where its glm api cost is competitive, and skipped where a cheaper or more reliable model wins.

Crucially, ClawRouters scores GLM-5.1 conservatively — capability 4, not 5, across coding and reasoning — precisely because its 58.4% SWE-Bench Pro number is self-reported and not yet independently confirmed. As our routing-judge accumulates production data, that score adjusts on evidence, not marketing. You get GLM-5.1's prices where it's genuinely the best fit, and something cheaper or more reliable everywhere else.

The result: teams cut their total LLM spend 40-60% versus pinning everything to one model — GLM-5.1 included — with no quality loss and no provider lock-in.

To see how this compares to pinning a single model or using a static gateway, read why OpenRouter won't cut your AI bill and our LLM API pricing guide for 2026.

Frequently Asked Questions

What is GLM-5.1 API pricing per million tokens in 2026? GLM-5.1 costs $1.40 per million input tokens and $4.40 per million output tokens from Z.ai (formerly Zhipu AI). It was released 2026-03-27, with weights open-sourced under MIT on 2026-04-07. The model has a 64K context window.

How much cheaper is GLM-5.1 than Claude or GPT? On input, GLM-5.1 ($1.40/M) is about 3.5x cheaper than Claude Opus 4.8 or GPT-5.5 ($5.00/M). On output, it's 5.7-6.8x cheaper. GLM-5.1 claims a 58.4% SWE-Bench Pro score, but that figure is self-reported and not yet independently corroborated.

Is GLM-5.1 cheaper than DeepSeek or Kimi? No. DeepSeek V4 Flash ($0.14/$0.28) and Kimi K2.6 ($0.60/$4.00) are both cheaper on input. GLM-5.1's advantages are its Chinese-language depth, open MIT weights, and a tighter output-to-input ratio — not the lowest absolute price.

Can I self-host GLM-5.1? Yes. Z.ai open-sourced GLM-5.1's weights under the permissive MIT license on 2026-04-07. If you have the GPU capacity, self-hosting eliminates per-token cost — but for most teams the hosted API stays cheaper than running the hardware.

How do I reduce my GLM-5.1 API cost? Use the cheaper GLM-4 Plus for simple tasks, cap output tokens, and route only the right requests to GLM-5.1. The biggest savings come from per-request routing across providers — which is exactly what ClawRouters automates.

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Pricing reflects Z.ai's published rates as of June 2026 and may change. The 58.4% SWE-Bench Pro figure is provider-reported and not independently verified at time of writing. ClawRouters keeps its routing pool and cost data current as providers update pricing.