Chat turn cost
1K fresh input + 500 output tokens
MiniMax M2.7
MiniMax M2.7 has the lower estimated token cost for this stated workload. Costs use the listed standard API rates.
Confidence: listed-rates
GLM-5V-Turbo vs MiniMax M2.7
Updated September 28, 2026. Rank says GLM-5V-Turbo is ahead. Price, access, and your workload can each overturn that. Public scores include evidence status and uncertainty.
Both of these models will change. Get the price, version and retirement notices for the pair, each with its source. Follow model changes
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Decision reading
GLM-5V-Turbo has the higher public score estimate, 48.87 versus 48.03, but the 90% score intervals overlap. Treat that as a lead, not a settled winner. 2 results are shared. Category rows resting on Estimated evidence or different benchmark sets are marked directional and do not name a winner.
- Shared results
- 2
- GLM-5V-Turbo only
- 0
- MiniMax M2.7 only
- 21
- Like-for-like categories
- 0 / 8
Estimated: GLM-5V-Turbo · Supported: MiniMax M2.7How the comparison works
Which one for your work
Recommendations appear only when a shared evidence basis or an explicit operating constraint supports the call. Secondary and unsupported use cases stay disclosed below the initial list.
Show secondary and unsupported calls
Repository review cost
50K fresh input + 3K output tokens
MiniMax M2.7
MiniMax M2.7 has the lower estimated token cost for this stated workload. Costs use the listed standard API rates.
Confidence: listed-ratesCoding work
Code generation, repair, and software-engineering tasks
Not enough matched evidence
GLM-5V-Turbo is scored on Estimated evidence for coding, so the reading is directional rather than like-for-like.
Confidence: limitedAgentic work
Tool use, computer use, and multi-step task completion
Not enough matched evidence
GLM-5V-Turbo is not ranked on the public lane for agentic, so no winner is named for agentic.
Confidence: limitedLong documents
Prompts that approach the documented context limit
No clear pick
The documented context windows are equal.
Confidence: documentedCache-heavy agent loop cost
200K cached + 20K fresh input + 10K output tokens
Not enough matched evidence
The page does not recommend a cost winner because at least one model cannot fit the stated workload in one request. GLM-5V-Turbo does not fit this workload in one request. MiniMax M2.7 does not fit this workload in one request. GLM-5V-Turbo has no published cached-input rate, so cached tokens use its listed input rate. MiniMax M2.7 has no published cached-input rate, so cached tokens use its listed input rate.
Confidence: rate-fallback
Which one for a specific job
Choose a job from the LLM Selector's task catalog to see the category row it rests on, under the same basis rules as the table below. A directional row stays directional; choosing a job never creates a winner.
The same task catalog as the LLM Selector. Each job names the evidence surface it rests on; nothing here adds a new score.
34.1GLM-5V-Turbo35.9MiniMax M2.7
Directional only · BenchAlign v5.7
MiniMax M2.7 scores higher, but at least one score rests on Estimated evidence or a different benchmark set. Directional only, no winner.
Coding scores combine specific tasks and setups. Match the editor, harness, and effort to your workflow.
Same basis rules as the category table below
What is actually comparable
Shared results can support a head-to-head reading. Results present for only one model describe coverage, not superiority.
2 Shared
21 MiniMax M2.7 only
3 categories rest on Estimated evidence or different evidence sets. Those rows remain visible for coverage context but do not name a winner.
Shape of the matched evidence
Only shared public evidence is shown. Sparse evidence stays a ruled list rather than being closed into a radar shape.
A shared-evidence shape is not available.
BenchLM does not draw a radar or infer missing axes when the matched evidence is too sparse.
Category results, on a stated basis
Each row shows the public-lane category score for both models: the BenchAlign v5.7 lane for agentic, coding, and knowledge, and the weighted public rows elsewhere, with the same rank each model page reports. A row is like-for-like only when both scores rest on Supported evidence or the same weighted set. Directional and not-comparable rows remain visible, but they never receive a winner.
Coding
Directional only- GLM-5V-Turbo
- 34.1
- Estimated · #82/142
- MiniMax M2.7
- 35.9
- Supported · #76/142
- Basis
- BenchAlign v5.7 lane · 0 vs 11 public rows
- Reading
- Directional only
Knowledge
Directional only- GLM-5V-Turbo
- 43.7
- Estimated · #81/168
- MiniMax M2.7
- 43.1
- Supported · #86/168
- Basis
- BenchAlign v5.7 lane · 0 vs 4 public rows
- Reading
- Directional only
Instruction following
Directional only- GLM-5V-Turbo
- 72.5
- #61/124
- MiniMax M2.7
- 91.6
- #10/124
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Directional only
Agentic
Not comparable- GLM-5V-Turbo
- Not ranked
- MiniMax M2.7
- 29.0
- Supported · #82/117
- Basis
- BenchAlign v5.7 lane · 2 vs 7 public rows
- Reading
- Not comparable
Reasoning
Not comparable- GLM-5V-Turbo
- 70.6
- Unranked · 2 rankable rows
- MiniMax M2.7
- 76.1
- Unranked · 2 rankable rows
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Not comparable
Multimodal
Not comparable- GLM-5V-Turbo
- 68.3
- Unranked · 1 rankable row
- MiniMax M2.7
- Not ranked
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Not comparable
Multilingual
Not comparable- GLM-5V-Turbo
- Not ranked
- MiniMax M2.7
- Not ranked
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Not comparable
Math
Not comparable- GLM-5V-Turbo
- Not ranked
- MiniMax M2.7
- Not ranked
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Not comparable
| Category | GLM-5V-Turbo | MiniMax M2.7 | Basis | Reading |
|---|---|---|---|---|
| Coding | 34.1Estimated · #82/142 | 35.9Supported · #76/142 | Directional onlyBenchAlign v5.7 lane · 0 vs 11 public rows | Directional only |
| Knowledge | 43.7Estimated · #81/168 | 43.1Supported · #86/168 | Directional onlyBenchAlign v5.7 lane · 0 vs 4 public rows | Directional only |
| Instruction following | 72.5#61/124 | 91.6#10/124 | Directional onlyProvisional lane · 0 vs 0 weighted rows | Directional only |
| Agentic | Not ranked | 29.0Supported · #82/117 | Not comparableBenchAlign v5.7 lane · 2 vs 7 public rows | Not comparable |
| Reasoning | 70.6Unranked · 2 rankable rows | 76.1Unranked · 2 rankable rows | Not comparableProvisional lane · 0 vs 0 weighted rows | Not comparable |
| Multimodal | 68.3Unranked · 1 rankable row | Not ranked | Not comparableProvisional lane · 0 vs 0 weighted rows | Not comparable |
| Multilingual | Not ranked | Not ranked | Not comparableProvisional lane · 0 vs 0 weighted rows | Not comparable |
| Math | Not ranked | Not ranked | Not comparableProvisional lane · 0 vs 0 weighted rows | Not comparable |
Ranks count the models scored in each category’s lane, so the agentic, coding, and knowledge denominators (BenchAlign v5.7) differ from the provisional-lane categories. Unranked scores sit on the provisional lane’s scale but never name a winner. Like-for-like rows within 0.5 points read as a practical tie.
Supported evidence per lane · bars run 0–100Methodology
What each workload costs
Three fixed token mixes turn per-token rates into comparable decisions. Each scenario states context fit and whether cached input had to fall back to the published list-input rate.
Chat turn
1K fresh input + 500 output tokens
- GLM-5V-Turbo
- $0.0032
- Fits in one request
- MiniMax M2.7
- $0.0009
- Fits in one request
MiniMax M2.7 has the lower modeled cost
Costs use the listed standard API rates.
Repository review
50K fresh input + 3K output tokens
- GLM-5V-Turbo
- $0.072
- Fits in one request
- MiniMax M2.7
- $0.0186
- Fits in one request
MiniMax M2.7 has the lower modeled cost
Costs use the listed standard API rates.
Cache-heavy agent loop
200K cached + 20K fresh input + 10K output tokens
- GLM-5V-Turbo
- $0.304
- Does not fit in one request
- Cached input priced at the published list-input rate
- MiniMax M2.7
- $0.078
- Does not fit in one request
- Cached input priced at the published list-input rate
GLM-5V-Turbo does not fit this workload in one request. MiniMax M2.7 does not fit this workload in one request. GLM-5V-Turbo has no published cached-input rate, so cached tokens use its listed input rate. MiniMax M2.7 has no published cached-input rate, so cached tokens use its listed input rate.
Cached input falls back to the list input rate only where a cached rate is unpublished
Specification differences
Sourced differences are shown directly. Missing facts stay explicit instead of being inferred from a model name or family.
SpecificationGLM-5V-TurboMiniMax M2.7
Context window
Maximum documented context; output-token limits may be lower.
GLM-5V-Turbo
200K
MiniMax M2.7
200K
API model ID
GLM-5V-Turbo
Not sourced
MiniMax M2.7
Not sourced
Cached-input rate
A missing cached-input rate falls back to the listed input rate only in the stated workload estimate.
GLM-5V-Turbo
Not published
MiniMax M2.7
Not published
Documented inputs
GLM-5V-Turbo
Not sourced
MiniMax M2.7
Not sourced
Documented outputs
GLM-5V-Turbo
Not sourced
MiniMax M2.7
Not sourced
Provider availability
GLM-5V-Turbo
Not sourced
MiniMax M2.7
Not sourced
Reasoning profile
GLM-5V-Turbo
Non-Reasoning
MiniMax M2.7
Non-Reasoning
Weight access
GLM-5V-Turbo
Proprietary
MiniMax M2.7
Open Weight
License
GLM-5V-Turbo
Proprietary
MiniMax M2.7
Open Weight
Release date
GLM-5V-Turbo
2026-03-01
MiniMax M2.7
2026-03-18
If you already use one of these models
- Deployment change
- The models list different providers, so authentication, endpoint behavior, limits, and feature support may change.
- Quality signal
- GLM-5V-Turbo has the higher public score estimate, 48.87 versus 48.03, but the 90% score intervals overlap.
- Workload cost
- Repository review: $0.072 vs $0.0186. Cache-heavy agent loop: $0.304 vs $0.078.
- Context tradeoff
- Both models list 200K.
Run the same representative tasks against both endpoints before changing production traffic.
Questions
Which is better, GLM-5V-Turbo or MiniMax M2.7?
GLM-5V-Turbo has the higher public score estimate, 48.87 versus 48.03, but the 90% score intervals overlap. The higher estimate is not a decisive winner because the uncertainty ranges overlap.
Which is better for coding, GLM-5V-Turbo or MiniMax M2.7?
MiniMax M2.7 scores higher for coding on the public lane, 35.9 to 34.1. GLM-5V-Turbo is scored on Estimated evidence for coding, so the reading is directional rather than like-for-like. BenchLM does not name a winner for coding from a directional reading; read the shared benchmark rows directly and test the models on the same task set.
Which is better for agentic tasks, GLM-5V-Turbo or MiniMax M2.7?
GLM-5V-Turbo is not ranked on the public lane for agentic tasks, so no winner is named for agentic tasks.
Which costs less, GLM-5V-Turbo or MiniMax M2.7?
For the stated presets, chat costs $0.0032 on GLM-5V-Turbo and $0.0009 on MiniMax M2.7; repository review costs $0.072 and $0.0186; the cache-heavy agent loop costs $0.304 and $0.078. GLM-5V-Turbo does not fit this workload in one request. MiniMax M2.7 does not fit this workload in one request. GLM-5V-Turbo has no published cached-input rate, so cached tokens use its listed input rate. MiniMax M2.7 has no published cached-input rate, so cached tokens use its listed input rate.
Which has the larger context window, GLM-5V-Turbo or MiniMax M2.7?
Both models list the same context window, 200K.
Benchmark evidence
The full public result ledger is available for audit without forcing a wide desktop table onto a phone.
Browse raw public benchmark evidence23 rows
Agentic
Claw-Eval
Shared sourceGLM-5V-Turbo53.8%MiniMax M2.748.7%GLM-5V-Turbo leads this result
Gert Labs
Shared sourceGLM-5V-Turbo30.76%MiniMax M2.740.40%MiniMax M2.7 leads this result
Terminal-Bench 2.0
GLM-5V-Turbo—MiniMax M2.757%SourceNot directly comparable
Toolathlon
GLM-5V-Turbo—MiniMax M2.746.3%SourceNot directly comparable
MLE-Bench Lite
GLM-5V-Turbo—MiniMax M2.766.6%SourceNot directly comparable
MM-ClawBench
GLM-5V-Turbo—MiniMax M2.762.7%SourceNot directly comparable
Terminal-Bench 2.1 (Vals)
GLM-5V-Turbo—MiniMax M2.748.7%SourceNot directly comparable
Coding
SWE-bench Verified*
GLM-5V-Turbo—MiniMax M2.775.4%SourceNot directly comparable
SWE-bench Pro
GLM-5V-Turbo—MiniMax M2.756.2%SourceNot directly comparable
SWE-Rebench
GLM-5V-Turbo—MiniMax M2.751.9%SourceNot directly comparable
SWE Multilingual
GLM-5V-Turbo—MiniMax M2.776.5%SourceNot directly comparable
Multi-SWE Bench
GLM-5V-Turbo—MiniMax M2.752.7%SourceNot directly comparable
VIBE-Pro
GLM-5V-Turbo—MiniMax M2.755.6%SourceNot directly comparable
NL2Repo
GLM-5V-Turbo—MiniMax M2.739.8%SourceNot directly comparable
Vibe Code Bench
GLM-5V-Turbo—MiniMax M2.727.04%SourceNot directly comparable
React Native Evals
GLM-5V-Turbo—MiniMax M2.771.4%SourceNot directly comparable
LiveCodeBench (Vals)
GLM-5V-Turbo—MiniMax M2.779.9%SourceNot directly comparable
SWE-bench (Vals)
GLM-5V-Turbo—MiniMax M2.773.8%SourceNot directly comparable
Knowledge
Math
AIME25 (Arcee)
GLM-5V-Turbo—MiniMax M2.780.0%SourceNot directly comparable
23 public results · 2 shared
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Last updated September 28, 2026