Model comparison

Gemma 4 31B vs GLM 4.7 Flash

Google's Gemma 4 31B against Z.ai's GLM 4.7 Flash: vendor list prices, limits, abilities and the Artificial Analysis indexes side by side - and what OpenSmartRoute does when both are available.

CheaperGLM 4.7 Flash

GLM 4.7 Flash lists at $0.145 per million tokens blended against $0.25 for Gemma 4 31B - 1.7x less.

Intelligence: levelEven

Intelligence index 15 against 15: within a point of each other.

Longer contextGemma 4 31B

Gemma 4 31B reads 262k tokens at once, GLM 4.7 Flash 200k.

Abilities differGemma 4 31B

only Gemma 4 31B offers image input.

NewerGemma 4 31B

Gemma 4 31B was released about 2 months later.

Side by side

Vendor list prices per million tokens and published limits; indexes are Artificial Analysis composites (0-100).

Specification	Gemma 4 31B	GLM 4.7 Flash
Input price / 1M tokens	$0.20	$0.06
Output price / 1M tokens	$0.40	$0.40
Blended (3 in : 1 out)	$0.25	$0.15
Cached input / 1M	-	-
Context window	262K	200K
Max output	16K	118K
Input	image, text, video	text
Output	text	text
Tool calls
Structured output
Reasoning
Open weights
Intelligence index	15	15
Coding index	43	-
Agentic index	4	-
Released	Apr 2, 2026	Jan 19, 2026
Available on this deployment

What a month costs

List price times tokens for three typical workloads; your mix of input and output decides which model's pricing suits you.

Workload	Gemma 4 31B	GLM 4.7 Flash	Difference
Support chat20M tokens in, 5M out a month - a help desk answering a few thousand conversations.	$6.00	$3.21	$2.79 less on GLM 4.7 Flash
Document summaries100M in, 5M out - long inputs, short answers.	$22.00	$8.05	$13.95 less on GLM 4.7 Flash
Coding assistant60M in, 20M out - a team's editor assistant, output-heavy.	$20.00	$11.63	$8.37 less on GLM 4.7 Flash

What the router does with the pair

The two measure the same on the intelligence index, so the router treats them as peers and lets price, observed latency and your outcomes decide: GLM 4.7 Flash wins on cost, and a few bad outcomes on either move the next requests to the other.

Price a request Playground

yaml

# targets.yaml - both models as routing targets (metadata.model links each to the catalogue entry)
targets:
  - { id: glm-4.7-flash, kind: llm, metadata: { model: z-ai/glm-4.7-flash } }
  - { id: gemma-4-31b-it, kind: llm, metadata: { model: google/gemma-4-31b-it } }

# rules.yaml - the cheaper model by default, the stronger one when the request is hard
rules:
  - name: simple-requests-stay-cheap
    when: { max_complexity: 0.45 }
    prefer: [glm-4.7-flash]
    weight: 0.8
  - name: hard-requests-escalate
    when: { min_complexity: 0.45 }
    prefer: [gemma-4-31b-it]
    weight: 0.8

Frequently asked

Is Gemma 4 31B cheaper than GLM 4.7 Flash?: No. Gemma 4 31B lists at $0.20 in / $0.40 out per million tokens, GLM 4.7 Flash at $0.06 in / $0.40 out; blended, GLM 4.7 Flash is the cheaper one.
Which is better, Gemma 4 31B or GLM 4.7 Flash?: On the Artificial Analysis intelligence index GLM 4.7 Flash scores higher (15 against 15). "Better" depends on the task: the coding and agentic indexes below and the abilities row may point the other way for your requests.
Which has the longer context window?: Gemma 4 31B - 262K tokens against 200K.
Can I use Gemma 4 31B and GLM 4.7 Flash together?: Yes. Declare both as targets and OpenSmartRoute picks one per request on quality for the task, price and your rules - the cheaper model for the requests it can answer, the stronger one for the rest - and records why in the trace.

Also compared

Every Google and Z.ai model, with prices, is on the vendor pages; the rankings show what the router actually picks.

For the business

What it means for which models you pay for.

Catalogue of every vendor
The right model, not the famous one
Price, context, abilities and benchmark indexes side by side, with the model that fits each task named.
The rankings
89% less
Lower cost per request
On the 150-prompt benchmark the router spent 89% less than sending every prompt to the reference model, and the judged quality went up, not down.
Read the benchmark
Per million tokens, every model
Prices you can compare
The catalogue prints input and output prices and what three typical monthly workloads cost on each.
The model catalogue

Which LLM is the cheapest for our workload?: The best-for pages rank the live catalogue per task by the index that measures it, and the router picks the cheapest model that meets your quality rule for each request.
How much can we save on our LLM bill?: On the public benchmark the router saved 89% against sending everything to the reference model. Import last month's provider bill on the bill page and the estimate is computed on your own traffic before you change a line of code.
How often do model prices change?: The catalogue is refreshed from the vendors' price lists; each model page shows the date of its data.

Stop choosing between Gemma 4 31B and GLM 4.7 Flash. Route to both.

Declare the pair as targets and OpenSmartRoute picks per request - the cheaper one where it holds up, the stronger one where it matters - with the comparison in every trace.

Create a free account Try a request

Free plan, no card. Fifteen thousand decisions a month with the full trace.

Specification

Gemma 4 31B

GLM 4.7 Flash

Input price / 1M tokens

$0.20

$0.06

Output price / 1M tokens

$0.40

Blended (3 in : 1 out)

$0.25

$0.15

Cached input / 1M

Context window

262K

200K

Max output

16K

118K

Input

image, text, video

text

Output

text

Tool calls

Structured output

Reasoning

Open weights

Intelligence index

Coding index

Agentic index

Released

Apr 2, 2026

Jan 19, 2026

Available on this deployment

Workload

Gemma 4 31B

GLM 4.7 Flash

Difference

Support chat20M tokens in, 5M out a month - a help desk answering a few thousand conversations.

$6.00

$3.21

$2.79 less on GLM 4.7 Flash

Document summaries100M in, 5M out - long inputs, short answers.

$22.00

$8.05

$13.95 less on GLM 4.7 Flash

Coding assistant60M in, 20M out - a team's editor assistant, output-heavy.

$20.00

$11.63

$8.37 less on GLM 4.7 Flash

# targets.yaml - both models as routing targets (metadata.model links each to the catalogue entry) targets: - { id: glm-4.7-flash, kind: llm, metadata: { model: z-ai/glm-4.7-flash } } - { id: gemma-4-31b-it, kind: llm, metadata: { model: google/gemma-4-31b-it } } # rules.yaml - the cheaper model by default, the stronger one when the request is hard rules: - name: simple-requests-stay-cheap when: { max_complexity: 0.45 } prefer: [glm-4.7-flash] weight: 0.8 - name: hard-requests-escalate when: { min_complexity: 0.45 } prefer: [gemma-4-31b-it] weight: 0.8

Frequently asked

Is Gemma 4 31B cheaper than GLM 4.7 Flash?

No. Gemma 4 31B lists at $0.20 in / $0.40 out per million tokens, GLM 4.7 Flash at $0.06 in / $0.40 out; blended, GLM 4.7 Flash is the cheaper one.

Which is better, Gemma 4 31B or GLM 4.7 Flash?

On the Artificial Analysis intelligence index GLM 4.7 Flash scores higher (15 against 15). "Better" depends on the task: the coding and agentic indexes below and the abilities row may point the other way for your requests.

Which has the longer context window?

Gemma 4 31B - 262K tokens against 200K.

Can I use Gemma 4 31B and GLM 4.7 Flash together?

Yes. Declare both as targets and OpenSmartRoute picks one per request on quality for the task, price and your rules - the cheaper model for the requests it can answer, the stronger one for the rest - and records why in the trace.

Gemma 4 31B vs GLM 4.7 Flash

Side by side

What a month costs

What the router does with the pair

Frequently asked

Also compared

What it means for which models you pay for.

The right model, not the famous one

Lower cost per request

Prices you can compare

Stop choosing between Gemma 4 31B and GLM 4.7 Flash. Route to both.

Gemma 4 31B vs GLM 4.7 Flash

Side by side

What a month costs

What the router does with the pair

Frequently asked

Also compared

What it means for which models you pay for.

The right model, not the famous one

Lower cost per request

Prices you can compare

Stop choosing between Gemma 4 31B and GLM 4.7 Flash. Route to both.