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GLM 5 Turbo vs Relace Search

Relace Search costs 1.3x less per output token than GLM 5 Turbo ($3.00 vs $4.00 per million). Updated October 22, 2026; no benchmark has tested both models yet.

GLM 5 TurboZ.ai
Coding
No data
Overall
No data
Output $/M
$4.00
Relace SearchRelace
Coding
No data
Overall
No data
Output $/M
$3.00
vs $4.00 vs $3.00 per 1M output tokens. Relace Search is 1.3x cheaper.

GLM 5 Turbo or Relace Search: which to pick

Neither model has a coding score yet, so the two cannot be ranked on coding.

Compare them on price, context and the boards below.

No benchmark has tested both yet. GLM 5 Turbo has results on Terminal-Bench Hard (Artificial Analysis run), ALE-Bench and AA Intelligence Index; Relace Search has none yet. Compare them on price and context instead.

Which one for your work

Each card is decided by one number both models carry. Where only one has it, the card says so and makes no call.

At a glance

Coding index
GLM 5 Turbo not benchmarked, Relace Search not benchmarked.
Widest category
None yet: no category has been tested on both models.
Widest single gap
None yet: no benchmark has tested both models.
Output price
GLM 5 Turbo $4.00 against Relace Search $3.00 per million output tokens, 1.3x apart.
Context window
GLM 5 Turbo takes 203K tokens, Relace Search 256K.

Benchmark by benchmark

Every board either model has a result on, a category to a fold; bars are each result on a 0 to 100 scale. Only one model is measured on each, so no row names a leader.

CodingDirectional onlySix of these boards feed the Vibe Coding Index2 boards measured
Coding benchmarks for GLM 5 Turbo and Relace Search
BenchmarkGLM 5 TurboRelace SearchLeader
Terminal-Bench Hard (Artificial Analysis run)33.3%Not measuredOnly GLM 5 Turbo measured
ALE-Bench634.0Not measuredOnly GLM 5 Turbo measured
Neither model has a result on the other 37 boards here
IntelligenceDirectional onlyPublic boards, shown as published, kept out of the index1 board measured
Intelligence benchmarks for GLM 5 Turbo and Relace Search
BenchmarkGLM 5 TurboRelace SearchLeader
AA Intelligence Index26.6Not measuredOnly GLM 5 Turbo measured
Neither model has a result on the other 1 board here
Agentic workDirectional onlyPublic boards, shown as published, kept out of the index1 board measured
Agentic work benchmarks for GLM 5 Turbo and Relace Search
BenchmarkGLM 5 TurboRelace SearchLeader
τ²-Bench Telecom98.5%Not measuredOnly GLM 5 Turbo measured
Neither model has a result on the other 40 boards here
ReasoningDirectional onlyPublic boards, shown as published, kept out of the index2 boards measured
Reasoning benchmarks for GLM 5 Turbo and Relace Search
BenchmarkGLM 5 TurboRelace SearchLeader
GPQA Diamond (Artificial Analysis run)84.7%Not measuredOnly GLM 5 Turbo measured
Humanity's Last Exam (Artificial Analysis run)27.8%Not measuredOnly GLM 5 Turbo measured
Neither model has a result on the other 20 boards here
Web designDirectional onlyPublic boards, shown as published, kept out of the index8 boards measured
Web design benchmarks for GLM 5 Turbo and Relace Search
BenchmarkGLM 5 TurboRelace SearchLeader
Design Arena: websites1262Not measuredOnly GLM 5 Turbo measured
Design Arena: UI components1260Not measuredOnly GLM 5 Turbo measured
Design Arena: game dev1248Not measuredOnly GLM 5 Turbo measured
Design Arena: data viz1264Not measuredOnly GLM 5 Turbo measured
Design Arena: 3D1247Not measuredOnly GLM 5 Turbo measured
Design Arena: all code categories1261Not measuredOnly GLM 5 Turbo measured
Design Arena: SVG1205Not measuredOnly GLM 5 Turbo measured
Design Arena: ASCII art1160Not measuredOnly GLM 5 Turbo measured
Neither model has a result on the other 1 board here
GeneralDirectional onlyPublic boards, shown as published, kept out of the index2 boards measured
General benchmarks for GLM 5 Turbo and Relace Search
BenchmarkGLM 5 TurboRelace SearchLeader
IFBench73.2%Not measuredOnly GLM 5 Turbo measured
Long context reasoning (AA-LCR)71.7%Not measuredOnly GLM 5 Turbo measured
Neither model has a result on the other 44 boards here

What each workload costs

Three fixed token mixes at each model's listed price, so per-token rates become a bill.

Chat turn

1K input + 500 output tokens

GLM 5 Turbo$0.0032Fits in one request
Relace Search$0.0025Fits in one request

Relace Search costs less

Repository review

50K input + 3K output tokens

GLM 5 Turbo$0.072Fits in one request
Relace Search$0.059Fits in one request

Relace Search costs less

Agent loop

200K input + 10K output tokens

GLM 5 Turbo$0.280Exceeds the context window
Relace Search$0.230Fits in one request

Relace Search costs less

Listed OpenRouter rates, without cache or batch discounts. A workload that does not fit a model's context window would need splitting.

Specification differences

The leading value in a row is marked; a row with a missing value names no leader. Our two rows are marked in red.

Specifications of GLM 5 Turbo and Relace Search
SpecificationZ.ai GLM 5 TurboRelace Search
MakerZ.aiRelace
WeightsNo public weights listedNo public weights listed
ReleasedMarch 15, 2026December 8, 2025
Context window203K256K
Input $/Mtok$1.20$1.00
Output $/Mtok$4.00$3.00
Coding indexNot benchmarkedNot benchmarked
Independent measurements behind it00
Cost per solved taskNot computableNot computable
SourceOpenRouter listingOpenRouter listing

Questions

Is GLM 5 Turbo better than Relace Search for coding?

Neither model has a coding score yet, so the two cannot be ranked on coding. Compare them on price, context and the boards below.

Which is cheaper, GLM 5 Turbo or Relace Search?

GLM 5 Turbo costs $1.20 per million input tokens and $4.00 per million output tokens; Relace Search costs $1.00 and $3.00. Cost per solved task is known for neither of them.

Which has the bigger context window, GLM 5 Turbo or Relace Search?

Relace Search, with 256K tokens against 203K.

Has any benchmark tested both GLM 5 Turbo and Relace Search?

No benchmark has tested both yet. GLM 5 Turbo has results on Terminal-Bench Hard (Artificial Analysis run), ALE-Bench and AA Intelligence Index; Relace Search has none yet. Compare them on price and context instead. Data as of October 22, 2026.

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