Updated October 10, 2026. Rank says GLM-5.3-Flash 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
GLM-5.3-Flash has the higher public point estimate, 57.36 versus 34.72. Their conditional score ranges overlap. These ranges do not establish rank confidence. 4 results are shared. Category rows resting on Estimated evidence or different benchmark sets are marked directional and do not name a winner.
Mistral
34.72/100
Estimated · Public rank #155
Conditional range 20.4–49.1
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.
Tool use, computer use, and multi-step task completion
GLM-5.3-Flash
GLM-5.3-Flash has the higher public agentic point estimate, 55.7 to 19.2, with Supported evidence for both models. The conditional ranges do not establish rank confidence.
Prompts that approach the documented context limit
GLM-5.3-Flash
GLM-5.3-Flash has the larger documented context window.
Code generation, repair, and software-engineering tasks
Not enough matched evidence
Mistral Medium 3.5 128B is scored on Estimated evidence for coding, so the reading is directional rather than like-for-like.
1K fresh input + 500 output tokens
Not enough matched evidence
A complete comparable API-rate estimate is not available for both models.
200K cached + 20K fresh input + 10K output tokens
Not enough matched evidence
A complete comparable API-rate estimate is not available for both models.
50K fresh input + 3K output tokens
Not enough matched evidence
A complete comparable API-rate estimate is not available for both models.
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.
Directional only · BenchAlign v5.8
GLM-5.3-Flash has the higher coding point estimate. Conditional score ranges do not establish rank confidence.
Coding scores combine specific tasks and setups. Match the editor, harness, and effort to your workflow.
Shared results can support a head-to-head reading. Results present for only one model describe coverage, not superiority.
1 category rests on Estimated evidence or different evidence sets. Those rows remain visible for coverage context but do not name a winner.
Only shared public evidence is shown. Sparse evidence stays a ruled list rather than being closed into a radar shape.
Too few matched category axes support a radar. The ruled list below shows only shared benchmark results; positions use each benchmark’s normalized display scale when available.
MMLU-Pro (Vals)Knowledge
Normalized gap 10.8Each row shows the public-lane category score for both models: the BenchAlign v5.8 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.
| Category | GLM-5.3-Flash | Mistral Medium 3.5 128B | Basis | Reading |
|---|---|---|---|---|
| Agentic | 55.7Supported · #34/123 | 19.2Supported · #103/123 | Like-for-likeBenchAlign v5.8 lane · 6 vs 3 public rows | GLM-5.3-Flash leads |
| Knowledge | 60.5Supported · #43/177 | 31.9Supported · #139/177 | Like-for-likeBenchAlign v5.8 lane · 2 vs 2 public rows | GLM-5.3-Flash leads |
| Coding | 49.3Supported · #42/146 | 23.6Estimated · #110/146 | Directional onlyBenchAlign v5.8 lane · 8 vs 2 public rows | Directional only |
| Reasoning | 81.1#11/28 | 73.7Unranked · 2 rankable rows | Not comparableProvisional lane · 0 vs 0 weighted rows | Not comparable |
| Multimodal | 84.0#14/54 | 60.1Unranked · 1 rankable row | Not comparableProvisional lane · 2 vs 0 weighted rows | Not comparable |
| Multilingual | Not ranked | Not ranked | Not comparableProvisional lane · 0 vs 0 weighted rows | Not comparable |
| Instruction following | Not ranked | 84.0#50/127 | 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 v5.8) 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.
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.
1K fresh input + 500 output tokens
GLM-5.3-Flash has no comparable published API token rate.
50K fresh input + 3K output tokens
GLM-5.3-Flash has no comparable published API token rate.
200K cached + 20K fresh input + 10K output tokens
Mistral Medium 3.5 128B has no published cached-input rate, so cached tokens use its listed input rate. GLM-5.3-Flash has no comparable published API token rate.
Sourced differences are shown directly. Missing facts stay explicit instead of being inferred from a model name or family.
Maximum documented context; output-token limits may be lower.
GLM-5.3-Flash
Mistral Medium 3.5 128B
256K
GLM-5.3-Flash
glm-5.3-flash
GLM-5.3-Flash model cardMistral Medium 3.5 128B
Not sourced
A missing cached-input rate falls back to the listed input rate only in the stated workload estimate.
GLM-5.3-Flash
No comparable hosted API rate
GLM-5.3-Flash model cardMistral Medium 3.5 128B
Not published
GLM-5.3-Flash
Not sourced
Mistral Medium 3.5 128B
Not sourced
GLM-5.3-Flash
Not sourced
Mistral Medium 3.5 128B
Not sourced
GLM-5.3-Flash
Not sourced
Mistral Medium 3.5 128B
Not sourced
GLM-5.3-Flash
Reasoning
Mistral Medium 3.5 128B
Reasoning
GLM-5.3-Flash
Open Weight
Mistral Medium 3.5 128B
Open Weight
GLM-5.3-Flash
Open Weight
Mistral Medium 3.5 128B
Open Weight
GLM-5.3-Flash
2026-08-26
Mistral Medium 3.5 128B
2026-04-29
GLM-5.3-Flash has the higher public point estimate, 57.36 versus 34.72. Their conditional score ranges overlap. These ranges do not establish rank confidence. The page therefore keeps the decision tied to the specific documented workload.
GLM-5.3-Flash scores higher for coding on the public lane, 49.3 to 23.6. Mistral Medium 3.5 128B 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.
GLM-5.3-Flash has the higher public agentic tasks point estimate, 55.7 to 19.2, with Supported evidence for both models. The conditional ranges do not establish rank confidence.
Both models do not have comparable published API token rates, so this page does not name a universal price winner.
GLM-5.3-Flash has the larger documented context window: 1M, compared with 256K.
The full public result ledger is available for audit without forcing a wide desktop table onto a phone.
Terminal-Bench 2.1
Not directly comparable
Toolathlon-Verified
Not directly comparable
AutomationBench
Not directly comparable
Agents' Last Exam
Not directly comparable
HLE w/ tools
Not directly comparable
Terminal-Bench 2.1 (Vals)
GLM-5.3-Flash leads this result
τ³-bench results
Not directly comparable
Gert Labs
Not directly comparable
Terminal-Bench 2.1
Not directly comparable
DeepSWE
Not directly comparable
NL2Repo
Not directly comparable
LiveCodeBench (Vals)
Not directly comparable
SWE-bench (Vals)
GLM-5.3-Flash leads this result
OpenHarmony Bench
Not directly comparable
FrontierSWE v2
Not directly comparable
Bug Hunt Bench
Not directly comparable
SWE-bench Verified
Not directly comparable
OfficeQA Pro
Not directly comparable
CharXiv
Not directly comparable
Chartography (tools)
Not directly comparable
BabyVision
Not directly comparable
MMVU
Not directly comparable
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Last updated October 10, 2026
