Provider choice for GLM-5.1 is a real economic decision. Across 10 benchmarked API providers, blended pricing runs from $0.74 to $1.70 per 1M tokens, output speed from 33.8 to 175.2 t/s, and the fastest provider is 5.2x quicker than the slowest. For teams deploying at scale, that spread determines whether this model fits a production budget or quietly wrecks a latency target.

GLM-5.1 is an April 2026 open-weight model from Z.AI, built for long-horizon, tool-using engineering work rather than short one-shot interactions. It carries a ~203K-token context window, MIT license, JSON and function calling support, and credible benchmark results on coding and agentic tasks — making it a strong candidate for teams who want deployment flexibility alongside cost discipline. For a deeper look at its predecessor, see the GLM-5 API benchmarks.

GLM-5.1 Executive Summary

GLM-5.1 is available across 10 benchmarked providers with blended pricing from $0.74 to $1.70 per 1M tokens. DeepInfra leads on cost, Fireworks on raw speed, Wafer on balance, and OpenRouter on managed access. The model is best suited for long-context, tool-using, engineering-heavy workflows where open weights and cost discipline both matter. Teams evaluating alternatives in the same family can compare against GLM-5 and GLM-4.6.

Best For	Provider	Why
Lowest price / cost-sensitive workloads	DeepInfra (FP8 benchmarked; FP4 on model page)	Lowest blended price at $0.74/1M tokens; lowest listed input ($1.05) and output ($3.50) pricing.
RAG, document-heavy, or agentic workloads	DeepInfra	Combines lowest token pricing with ~202.8K context, cached input at $0.205/1M, JSON, and function calling.
Proprietary or managed access	OpenRouter	Managed routing to z-ai/glm-5.1 with provider fallbacks for maximum uptime.
Easiest onboarding	DeepInfra	Public deployment of zai-org/GLM-5.1 with full JSON and function calling — no provider selection logic needed.
Lowest latency / fastest responses	Fireworks	Leads on output speed (175.2 t/s) and time to first answer token (22.58s).
Best balanced alternative	Wafer	#2 on blended price ($0.86), output speed (160.9 t/s), and time to first answer token (24.67s).
Speed-focused backup	FriendliAI	#3 on output speed (128.2 t/s) and answer latency (30.62s) at a competitive $0.90 blended price.

Understanding Tokens and How You’re Charged

Token pricing is where a model that looks cheap on paper gets expensive in production. GLM-5.1 is built for long sessions, large prompts, and tool use — all of which consume tokens aggressively.

A token is a chunk of text, not a word. Prompts, tool schemas, retrieved documents, chain state, and model output all count toward your bill. For long-context agent workflows, the expensive part is often not the final answer — it is the repeated accumulation of context.

• Input tokens: Everything you send to the model — prompts, system messages, tool definitions, retrieved context, conversation history.
• Output tokens: Everything the model generates. Long code completions, structured JSON, and step-by-step agent summaries push this up fast.
• Cached input tokens: Previously seen input billed at a discount. If your app resends the same system prompt, tool definitions, or repo map repeatedly, cache pricing can change the total economics significantly. DeepInfra is the only provider here that explicitly lists cached input pricing for GLM-5.1: $0.205 per 1M tokens.

Artificial Analysis also uses a 7:2:1 cache-input-output ratio in its blended benchmark price — a reminder that cache behavior is the workload, not an edge case.

Token type	What it is	Why it matters
Input tokens	Tokens you send to the model in the request	Your prompt cost. Includes user input, system prompts, tool definitions, retrieved context, and prior conversation state.
Output tokens	Tokens the model generates in response	Usually the most expensive token class per token. Long answers, code generation, and verbose tool reasoning push this up fast.
Cached input tokens	Previously seen input tokens billed at a discounted rate	Matters for chat loops, agents, and RAG systems that resend large prompt prefixes. Can materially reduce costs.

Provider Token Cost Tradeoffs for GLM-5.1

Different GLM-5.1 providers favor different workload shapes — input-heavy, output-heavy, or cache-friendly. Choosing on blended price alone can mislead if your traffic mix is unusual.

Provider	Token cost profile	Advantages	Disadvantages
DeepInfra	$1.05 input / $3.50 output / $0.205 cached per 1M. Lowest blended at $0.74/1M.	Best for cost-sensitive production. Cached input pricing is the standout lever for RAG, agent loops, and long sessions.	Benchmarked lowest-cost result references FP8; model page lists FP4. Confirm exact serving tier before locking in cost assumptions.
OpenRouter	$0.98 input / $3.08 output per 1M. No cache pricing listed.	Lower listed input/output rates than DeepInfra’s model page. Useful for routed access and provider fallback.	No published cache pricing, so repeated-prefix workloads are harder to model. Less direct control over the underlying provider path.
Wafer	Blended $0.86/1M (Artificial Analysis). No input/output breakout.	Good price/speed balance — #2 on both blended cost and output speed.	No separate input/output/cache rates available. Hard to model for unusual token mixes.
FriendliAI	Blended $0.90/1M (Artificial Analysis). No token-type breakout.	Low enough to stay practical; #3 on output speed and answer latency.	Same visibility gap as Wafer — blended price alone can mislead on asymmetric workloads.
SiliconFlow	Blended $0.90/1M. No token-type breakout.	Competitive on blended cost.	No JSON mode (the only provider in the set without it). Missing structured output creates retry logic and prompt padding that inflates real token counts.
Novita	Blended $0.90/1M. No token-type breakout.	Sits in the low-cost group.	No detailed input/output/cache pricing available. Harder to budget for prompt-heavy or output-heavy workloads.
Fireworks	Not lowest-cost tier on blended pricing.	Best provider when latency is the primary constraint — 175.2 t/s output speed.	You are paying for speed. For large-scale async workloads, the speed premium may not justify the token bill.
Together.ai	$1.40 input / $4.40 output per 1M.	Straightforward published token pricing.	More expensive than DeepInfra on both sides. Gap compounds on prompt-heavy or code-heavy workloads.
Nebius	$1.40 input / $4.40 output per 1M. Highest blended at $1.70/1M.	No cost advantage.	Most expensive provider in the set. Premium adds up fast on long-context or agentic workloads.
Parasail	No token-type breakout.	No cost advantage called out in the benchmark data.	Slowest measured output speed. Low-ish token rates, but worse user experience for interactive use.

Practical rule of thumb

• Prompt-heavy workloads: Input pricing matters most. DeepInfra and OpenRouter are strong here.
• Output-heavy workloads: Output pricing dominates. Parasail and DeepInfra have the lowest published output rates.
• Cache-friendly workloads: DeepInfra is the only provider with explicit cached input pricing for GLM-5.1. This matters most for multi-turn agents, RAG loops, and any app that resends large stable prefixes.
• Avoid Nebius and Together.ai if you are cost-sensitive — both sit well above the benchmark median on blended price.
• Check SiliconFlow carefully: it is the only provider in this set without JSON mode, which creates downstream friction in structured output pipelines.

The biggest pricing trap with GLM-5.1: teams focus on the per-1M headline, then build an agent that resends huge prompt prefixes, emits long tool traces, and act surprised when “cheap” becomes a line item. Model at least three workload shapes before committing to a provider.

DeepInfra: the power user’s choice for GLM-5.1

DeepInfra runs on bare-metal infrastructure, typically 50–80% cheaper than major cloud competitors, and is the only provider in this benchmark set with explicit cached input pricing for GLM-5.1. For developers building long-session, tool-using, or agentic workloads, that combination of low token cost and cache-aware economics is the clearest cost lever available.

Model	Best Use Case	Context Window	Input ($/1M)	Output ($/1M)
GLM-5.1	Long-horizon agentic engineering and tool-using workflows	202,752 tokens	$1.05	$3.50

At $1.05 input / $3.50 output per 1M tokens and a $0.74/1M blended benchmark price under Artificial Analysis’s 7:2:1 cache-input-output mix, DeepInfra gives you more room to scale before token spend becomes the bottleneck. Browse the full text generation model catalog to see how GLM-5.1 compares against other options for your workload.

Real-World Cost Scenarios for Developers

The scenarios below reflect workloads where DeepInfra’s GLM-5.1 pricing is easiest to justify: long prompts, repeated context, tool-heavy loops, and engineering workflows that keep state across turns.

Scenario 1: Repo-aware coding assistant

A coding assistant that ingests repo context, tool schemas, and prior conversation state on every turn. This is exactly the workload GLM-5.1’s long-context design is built for, and where DeepInfra’s low input pricing keeps monthly spend predictable.

Metric	Value
Volume	10,000 requests/month
Model	GLM-5.1
Provider	DeepInfra
Input Tokens	200,000,000
Output Tokens	40,000,000
Monthly Cost	$350.00

Cost breakdown:

• Input: 200M × $1.05/1M = $210.00
• Output: 40M × $3.50/1M = $140.00
• Total: $350.00/month

Comparison: The same workload on Together.ai would cost $456.00 — $106.00 more per month.

Scenario 2: Long-document RAG assistant

Input-heavy workloads where policy docs, runbooks, incident history, and retrieved passages dominate the token mix. DeepInfra is strong here because the input side is cheap and the context window is large enough to reduce aggressive chunking. For guidance on building document-processing pipelines, see open vs. closed source model tradeoffs.

Metric	Value
Volume	50,000 requests/month
Model	GLM-5.1
Provider	DeepInfra
Input Tokens	500,000,000
Output Tokens	50,000,000
Monthly Cost	$700.00

Cost breakdown:

• Input: 500M × $1.05/1M = $525.00
• Output: 50M × $3.50/1M = $175.00
• Total: $700.00/month

Comparison: The same workload on Nebius would cost $920.00 — $220.00 more per month.

Scenario 3: Agent loop with stable prompt prefixes

Where DeepInfra gets especially compelling. If your agent resends the same system prompt, repo map, tool definitions, and workflow instructions on every turn, cached input pricing becomes a structural cost lever. This is exactly the pattern GLM-5.1 is designed for.

Metric	Value
Volume	100,000 agent turns/month
Model	GLM-5.1
Provider	DeepInfra
Input Tokens	100M fresh + 300M cached
Output Tokens	20,000,000
Monthly Cost	$236.50

Cost breakdown:

• Cached input: 300M × $0.205/1M = $61.50
• Fresh input: 100M × $1.05/1M = $105.00
• Output: 20M × $3.50/1M = $70.00
• Total: $236.50/month

Comparison: The same blended token volume on Nebius ($1.70/1M) would cost materially more. DeepInfra at $0.74/1M blended is the lowest in the benchmark set under the 7:2:1 cache-input-output mix Artificial Analysis uses.

Scenario 4: Batch code generation and refactoring

Asynchronous overnight jobs — generating tests, migrating code, writing adapters, refactoring templates. Speed matters less than price discipline here. This is one of the cleanest cases for DeepInfra over a faster but less cost-efficient option.

Metric	Value
Volume	25,000 jobs/month
Model	GLM-5.1
Provider	DeepInfra
Input Tokens	125,000,000
Output Tokens	75,000,000
Monthly Cost	$393.75

Cost breakdown:

• Input: 125M × $1.05/1M = $131.25
• Output: 75M × $3.50/1M = $262.50
• Total: $393.75/month

Comparison: The same workload on Together.ai would cost $505.00 — $111.25 more per month.

Scenario 5: Production API at scale

At production scale, small per-token differences compound fast. DeepInfra’s blended price lead becomes operationally meaningful.

Metric	Value
Volume	1,000,000,000 total tokens/month
Model	GLM-5.1
Provider	DeepInfra
Token Mix	7:2:1 cache-input-output
Monthly Cost	$740.00 blended benchmark equivalent

Cost basis:

• 1,000M tokens × $0.74/1M blended = $740.00

Comparison: Same volume on Wafer = $860.00. On Nebius = $1,700.00.

For context on how other models in the GLM family perform at this scale, the GLM-4.6 vs DeepSeek-V3.2 comparison breaks down cost and performance tradeoffs across providers.

Conclusion

Choosing a provider for GLM-5.1 comes down to three things: input cost, cache pricing, and latency fit. The spread across 10 providers is wide enough that a naive choice costs real money, and the model’s design for long-horizon tool-using work means the token patterns that hurt most — large inputs, repeated prefixes, verbose tool traces — are exactly where provider differences compound fastest.

For most agentic and engineering workloads, DeepInfra is the strongest starting point: lowest blended and input prices, explicit cached input pricing at $0.205/1M, full JSON and function calling support, and private endpoint deployment for teams that need it. For latency-sensitive interactive applications, Fireworks leads. For managed routing with fallbacks, OpenRouter is the practical choice.

Before building, check the GLM-5.1 API reference on DeepInfra to confirm supported parameters, then model your actual token costs — input-heavy, output-heavy, or cache-friendly — against the pricing tiers covered here. The GLM-5 benchmarks post is also useful context if you are deciding between generations.