GLM-4.5 vs Claude Opus: The Price Gap That Feels Illegal
A full teardown of GLM-4.5 vs Claude Opus across light coding, heavy reasoning, and scale. Spoiler: GLM-4.5 is still cheaper, anywhere from 5× to 100× depending on the workload.
There are moments in tech where you stare at the numbers and think, nah, this can’t be real.
That’s exactly what happened when I compared my GLM-4.5 usage against Claude Opus. The gap isn’t just wide—it feels illegal.
Quick Receipts (What I Paid)
Claude 4.1 Opus (Cursor)
- Total tokens: 10,506,082
- Total output: 49,692
- Cost: $34.49
GLM-4.5 (OpenRouter, one morning run)
- Total tokens: ~989,447
- Total cost: $0.0304
- Avg per request: ~49,472 tokens for $0.00152
Read that again: ~1M tokens for three cents.
Cost Math — Clean and Comparable
Metric | Claude Opus | GLM-4.5 (morning run) | Delta |
---|---|---|---|
Cost / 1k tokens | $0.003285 | $0.0000307 | ~107× cheaper |
Tokens per $1 | ~304,400 | ~32,520,000 | ~107× more tokens |
At the extremes, GLM-4.5 looked like it was printing tokens for free. But then I looked at my sustained usage across millions of tokens.
Real-World Logs (21M+ tokens)
From my dashboard:
Model | Tokens | Cost ($) | Cost / 1k | Tokens / $1 | Savings vs Opus |
---|---|---|---|---|---|
glm-4.5 | 21M | 12.90 | $0.000614 | ~1.63M | ~5.3× cheaper |
glm-4.5-air | 4.41M | 0.831 | $0.000188 | ~5.31M | ~17× cheaper |
glm-4.5v | 3.07K | 0.00297 | $0.000967 | ~1.03M | ~3.4× cheaper |
chatgpt-4o-latest | 1.99K | 0.0365 | $0.018342 | ~54.5K | Opus-level expensive |
Light Coding vs Heavy Reasoning
Here’s where it gets interesting:
- Morning Run (Light Coding): Minimal overhead, lean prompts, straight coding tasks. Result: insane efficiency (107× cheaper than Opus).
- Sustained Logs (Heavy Reasoning): Bigger prompts, longer contexts, deeper reasoning steps. Efficiency drops—but still between 5× and 17× cheaper than Opus.
👉 Translation: even when I crank up context windows and force the model to think harder, GLM-4.5 never loses its cost edge. At worst, it’s 5× cheaper. At best, it’s 100× cheaper.
Price–Performance Index (PPI)
Define PPI = tokens per dollar. Higher is better.
- Claude Opus PPI: ~304,400
- GLM-4.5 PPI (morning run): ~32.5M
- GLM-4.5 PPI (sustained): 1.6M to 5.3M depending on variant
Even the “worst case” GLM-4.5 run beats Opus several times over.
Scenario Modeling
What if you run a heavy coding + reasoning sprint of 200M tokens?
- Opus: ~$657
- GLM-4.5 (sustained rate): ~$122 (glm-4.5) → savings ~$535
- GLM-4.5-air: ~$38 → savings ~$619
- Morning Run rates: ~$6 → savings ~$651
Either way, you’re keeping thousands in your pocket every sprint.
Why the Numbers Diverge
- Input-heavy prompts: Reasoning workloads inflate input tokens, raising costs.
- Model variants: GLM-4.5-air is absurdly efficient, while vanilla GLM-4.5 is still solid.
- Free-tier quirks: Some morning runs clearly benefitted from free/experimental credits, making the numbers almost comical.
But here’s the kicker: even without freebies, GLM-4.5 is still crushing Opus on cost.
Quality Notes (My Experience)
- Accepted ~3k lines of code already.
- Agentic loops (“do, check, fix”) feel natural—tighter and fewer retries.
- Performs better the more I throw at it, especially in multi-step reasoning.
Drawback: No image upload support yet. If your workflow depends on multimodal, that’s a limitation. But for coding? Hands down, GLM-4.5 is insane.
Caveats (Fair Play)
- Pricing depends on provider (Cursor vs OpenRouter).
- Heavy reasoning loads skew token usage—so always benchmark against your workflow.
- Cheap tokens don’t excuse bad ops: sandbox before prod.
The Bottom Line
- Light coding workloads: GLM-4.5 is ~100× cheaper than Opus.
- Heavy reasoning workloads: GLM-4.5 is still 5–17× cheaper.
- Coding quality: On par with, or better than, Opus.
When the economics range from “unfair advantage” to “still massively cheaper,” the conclusion is simple:
Shift coding workloads to GLM-4.5. Keep Opus only for niches. Bank the savings.
Deeper Cut: Light Coding vs Heavy Reasoning
Not all tokens are equal. Two regimes emerged from my tests:
Regime | Example Workload | Cost / 1k tokens | Tokens per $1 | vs Opus (× cheaper) |
---|---|---|---|---|
GLM‑4.5 (Best‑case) | Short prompts, pure code‑gen, tight loops | $0.00003074 | ~32,531,547 | ~106.9× |
GLM‑4.5 (Sustained) | Larger inputs, RAG/context, more chain‑of‑thought | $0.00061429 | ~1,627,907 | ~5.35× |
GLM‑4.5 Air | Lighter tier, similar usage patterns | $0.00018844 | ~5,306,859 | ~17.43× |
Claude Opus | Cursor, thinking mode | $0.00328500 | ~304,414 | 1× |
Interpretation: The tighter and more code‑focused your loop, the closer you get to the best‑case numbers. As you add heavier context and higher‑order reasoning, effective cost rises—but GLM‑4.5 still beats Opus handily.
Scenario Planner (10M / 100M / 1B Tokens)
Tokens ▶ | Opus | GLM‑4.5 (Best) | GLM‑4.5 (Sustained) | GLM‑4.5 Air |
---|---|---|---|---|
10M | $32.85 | $0.3074 | $6.1429 | $1.8844 |
100M | $328.50 | $3.0739 | $61.4286 | $18.8435 |
1B | $3,285.00 | $30.7394 | $614.2857 | $188.4354 |
Even at sustained GLM‑4.5 rates, the 1B‑token bill is ~5.3× lower than Opus. At best‑case, it’s ~107× lower.
Methodology & Assumptions
- Best‑case GLM‑4.5 comes from a morning batch (≈989,447 tokens for $0.0304). Minimal input, straight code gen.
- Sustained GLM‑4.5 uses your dashboard roll‑up (≈21M tokens for $12.90). Heavier prompts, more context windows, more reasoning.
- GLM‑4.5 Air uses your dashboard roll‑up (≈4.41M tokens for $0.831).
- Opus uses your Cursor stat (≈10.5M tokens for $34.49).
- All costs normalized to $/1k tokens and projected to fixed token targets for apples‑to‑apples.
When Opus Still Makes Sense
- You need image uploads / multimodal right now (GLM‑4.5: no image upload).
- Specific analysis / safety profiles where Opus is mandated.
- Teams standardized on Anthropic tooling with strong cache wins on familiar prompts.
Practical Guidance
- For autofix, refactors, and green‑field scaffolds: default to GLM‑4.5.
- For heavier design‑doc reasoning but still code‑adjacent: try GLM‑4.5 Air; if quality holds, bank the ~17× savings.
- Keep Opus as a surgical tool for multimodal or niche reasoning cases.
- Track tokens/$ (PPI) per repo over time; set an internal SLO like: “≥ 1.5M tokens per $ on code tasks.”
Appendix: Repro Math
- Opus cost/1k = $34.49 ÷ (10,506,082/1,000) = $0.003285.
- GLM‑4.5 best cost/1k = $0.030415 ÷ (989,447/1,000) = $0.00003074.
- GLM‑4.5 sustained cost/1k = $12.90 ÷ (21,000,000/1,000) = $0.00061429.
- GLM‑4.5 Air cost/1k = $0.831 ÷ (4,410,000/1,000) = $0.00018844.
Bottom line stays the same—GLM‑4.5 is the better coder and the cheaper operator.
Sample Code Output of glm-4.5 below
Sample Code: Ruby on Rails Service Object vs Node.js Equivalent
Here's a practical example of a user management service object in both Ruby on Rails and Node.js, demonstrating how GLM-4.5 handles cross-language patterns.
Ruby on Rails Service Object
# app/services/user_management_service.rb
class UserManagementService
include ActiveModel::Model
include ActiveModel::Attributes
attribute :user_id, :integer
attribute :action, :string
attribute :metadata, :hash, default: {}
validates :user_id, presence: true
validates :action, presence: true, inclusion: { in: %w[activate deactivate suspend upgrade] }
def initialize(user_id, action, metadata = {})
@user_id = user_id
@action = action
@metadata = metadata
@errors = ActiveModel::Errors.new(self)
end
def call
return { success: false, errors: errors.full_messages } unless valid?
User.transaction do
user = User.find_by(id: user_id)
return { success: false, errors: ["User not found"] } unless user
case action
when 'activate'
result = activate_user(user)
when 'deactivate'
result = deactivate_user(user)
when 'suspend'
result = suspend_user(user)
when 'upgrade'
result = upgrade_user(user)
end
log_action(user, result)
send_notification(user, action) if result[:success]
result
end
rescue StandardError => e
Rails.logger.error "UserManagementService failed: #{e.message}"
{ success: false, errors: [e.message] }
end
private
def activate_user(user)
return { success: false, errors: ["User already active"] } if user.active?
user.update!(
status: 'active',
activated_at: Time.current,
activation_token: nil
)
{ success: true, message: "User activated successfully", user: user }
end
def deactivate_user(user)
return { success: false, errors: ["User already inactive"] } unless user.active?
user.update!(
status: 'inactive',
deactivated_at: Time.current
)
{ success: true, message: "User deactivated successfully", user: user }
end
def suspend_user(user)
return { success: false, errors: ["User already suspended"] } if user.suspended?
user.update!(
status: 'suspended',
suspended_at: Time.current,
suspension_reason: metadata[:reason]
)
{ success: true, message: "User suspended successfully", user: user }
end
def upgrade_user(user)
plan = metadata[:plan]
return { success: false, errors: ["Plan is required"] } unless plan
user.update!(
plan: plan,
upgraded_at: Time.current,
previous_plan: user.plan
)
{ success: true, message: "User upgraded to #{plan}", user: user }
end
def log_action(user, result)
AuditLog.create!(
user_id: user.id,
action: action,
metadata: metadata.merge(result: result[:success]),
ip_address: metadata[:ip_address]
)
end
def send_notification(user, action)
UserMailer.send(
"#{action}_notification",
user: user,
metadata: metadata
).deliver_later
end
end
Node.js Equivalent
// services/UserManagementService.js
const { User, AuditLog } = require("../models");
const { sendEmail } = require("../utils/email");
const logger = require("../utils/logger");
const { ValidationError } = require("../utils/errors");
class UserManagementService {
constructor(userId, action, metadata = {}) {
this.userId = userId;
this.action = action;
this.metadata = metadata;
this.errors = [];
}
async call() {
try {
if (!this.isValid()) {
return { success: false, errors: this.errors };
}
const result = await User.transaction(async (transaction) => {
const user = await User.findByPk(this.userId, { transaction });
if (!user) {
return { success: false, errors: ["User not found"] };
}
let actionResult;
switch (this.action) {
case "activate":
actionResult = await this.activateUser(user, transaction);
break;
case "deactivate":
actionResult = await this.deactivateUser(user, transaction);
break;
case "suspend":
actionResult = await this.suspendUser(user, transaction);
break;
case "upgrade":
actionResult = await this.upgradeUser(user, transaction);
break;
default:
throw new Error(`Invalid action: ${this.action}`);
}
await this.logAction(user, actionResult, transaction);
if (actionResult.success) {
await this.sendNotification(user, this.action);
}
return actionResult;
});
return result;
} catch (error) {
logger.error(`UserManagementService failed: ${error.message}`);
return { success: false, errors: [error.message] };
}
}
isValid() {
if (!this.userId) {
this.errors.push("User ID is required");
}
if (!this.action) {
this.errors.push("Action is required");
} else if (!["activate", "deactivate", "suspend", "upgrade"].includes(this.action)) {
this.errors.push("Invalid action");
}
return this.errors.length === 0;
}
async activateUser(user, transaction) {
if (user.status === "active") {
return { success: false, errors: ["User already active"] };
}
await user.update(
{
status: "active",
activatedAt: new Date(),
activationToken: null,
},
{ transaction }
);
return {
success: true,
message: "User activated successfully",
user: user.toJSON(),
};
}
async deactivateUser(user, transaction) {
if (user.status !== "active") {
return { success: false, errors: ["User already inactive"] };
}
await user.update(
{
status: "inactive",
deactivatedAt: new Date(),
},
{ transaction }
);
return {
success: true,
message: "User deactivated successfully",
user: user.toJSON(),
};
}
async suspendUser(user, transaction) {
if (user.status === "suspended") {
return { success: false, errors: ["User already suspended"] };
}
await user.update(
{
status: "suspended",
suspendedAt: new Date(),
suspensionReason: this.metadata.reason,
},
{ transaction }
);
return {
success: true,
message: "User suspended successfully",
user: user.toJSON(),
};
}
async upgradeUser(user, transaction) {
const plan = this.metadata.plan;
if (!plan) {
return { success: false, errors: ["Plan is required"] };
}
await user.update(
{
plan: plan,
upgradedAt: new Date(),
previousPlan: user.plan,
},
{ transaction }
);
return {
success: true,
message: `User upgraded to ${plan}`,
user: user.toJSON(),
};
}
async logAction(user, result, transaction) {
await AuditLog.create(
{
userId: user.id,
action: this.action,
metadata: {
...this.metadata,
result: result.success,
},
ipAddress: this.metadata.ipAddress,
},
{ transaction }
);
}
async sendNotification(user, action) {
try {
await sendEmail({
template: `${action}Notification`,
to: user.email,
data: {
user: user.toJSON(),
metadata: this.metadata,
},
});
} catch (error) {
logger.error(`Failed to send notification: ${error.message}`);
}
}
}
module.exports = UserManagementService;
Both implementations handle the same core functionality:
- User state management (activate, deactivate, suspend, upgrade)
- Transactional database operations
- Error handling and validation
- Audit logging
- Notification sending
The Ruby version leverages Rails conventions and ActiveModel, while the Node.js version uses a more explicit approach with async/await patterns. Both are production-ready and demonstrate how GLM-4.5 can generate idiomatic code across different ecosystems.
~ FIN ~