GLM-5.3-Flash vs MiniMax M2.7
GLM-5.3-Flash wins on Overall, Coding, Multimodal. Every numeric field is compared below, with a worked monthly-cost example and a pick rule for each use case.
Verdict at a glance
Choose GLM-5.3-Flash if you…
- Among the cheapest
- Fast
- Open source
- Best for: Ultra-fast, Ultra-low-cost, High concurrency
Choose MiniMax M2.7 if you…
- Light and fast
- Open source
- Low price
- Best for: Light tasks, Open source, Low cost
Head-to-head aggregate scores
Scores are 0–100, aggregated from public benchmark information and independently weighted across three leaderboards. Rank is out of 22 tracked models.
Aggregated from public sources and independently weighted; methodology on the Terms page. Scores within 3 points are treated as statistically tied.
Specs & pricing — every field side by side
List API prices in USD per 1M tokens. The highlighted cell is the stronger value/capability on that row.
| Dimension | GLM-5.3-Flash | MiniMax M2.7 | Verdict |
|---|---|---|---|
| Vendor | Zhipu AI (CN) | MiniMax (CN) | Different vendors |
| Released | 2026.08 | 2026.05 | GLM-5.3-Flash is newer |
| Overall (rank) | 65 · #18 | 64 · #19 | GLM-5.3-Flash +1 |
| Coding | 60 · #20 | 59 · #21 | GLM-5.3-Flash +1 |
| Multimodal | 63 · #21 | 62 · #22 | GLM-5.3-Flash +1 |
| Context window | 128K | 205K | MiniMax M2.7 larger |
| Max output | 64K | 32K | GLM-5.3-Flash longer |
| Effective-context | 80 | 85 | MiniMax M2.7 more reliable |
| Input $/1M | $0.07 | $0.27 | GLM-5.3-Flash cheaper |
| Output $/1M | $0.25 | $1.08 | GLM-5.3-Flash cheaper |
| Cache discount | none | none | Tie |
| Speed | ~90 tok/s | ~70 tok/s | GLM-5.3-Flash faster |
| TTFT | 0.2s | 0.4s | GLM-5.3-Flash snappier |
| Function calling | 70 | 72 | MiniMax M2.7 ahead |
| Refusal rate | ~6% | ~6% | Tie |
| English | 65 | 65 | Tie |
| Chinese | 85 | 82 | GLM-5.3-Flash |
| Modalities | text | text | Same |
| Open weights | Yes | Yes | Both open |
| Fine-tuning | Yes | Yes | |
| Free tier | ChatGLM free | Hailuo AI free | |
| SOC2 / no-train | no / yes | no / yes | |
| Private deployment | Yes | Yes | Both support it |
Fields drawn from vendor public documentation and the Modelspectra 22-model dataset; speed varies with network, concurrency and prompt length. Verify current pricing before purchase.
Dimension-by-dimension analysis
Reasoning & overall intelligence
GLM-5.3-Flash leads the overall aggregate by 1 points (65 vs 64). That is a meaningful, not marginal, edge on hard, multi-step reasoning, while MiniMax M2.7 remains a strong generalist that is not out of its depth on routine work.
Agentic coding
This is a modest gap: GLM-5.3-Flash scores 60 against 59. On multi-file edits, SWE-style tickets and long-horizon agent loops GLM-5.3-Flash needs fewer correction turns; MiniMax M2.7 is still competent for scripts and assisted completion but trails as task complexity rises.
Multimodal
GLM-5.3-Flash leads multimodal 63 vs 62. Neither emits native video, so the comparison is about parsing images and documents, not generation.
Speed & latency
GLM-5.3-Flash is faster in interactive use: ~90 tok/s with 0.2s TTFT versus ~70 tok/s with 0.4s TTFT (about 1.3× the throughput). For conversational UIs where perceived responsiveness drives retention, that edge is a real product factor even when raw reasoning is lower.
Context: window vs usable recall
Nominal windows are 128K for GLM-5.3-Flash and 205K for MiniMax M2.7. Effective-context scores point the same way as window size — MiniMax M2.7 is ahead on usable recall (85 vs 80), so prefer it for long-document work where details cannot be missed.
Price & total cost
GLM-5.3-Flash is the cheaper API at $0.07/$0.25 versus MiniMax M2.7 at $0.27/$1.08 per 1M input/output tokens — list input is about 3.6× lower.
Chinese vs English
English: GLM-5.3-Flash 65 vs MiniMax M2.7 65. Chinese: 85 vs 82. For Chinese-language production, GLM-5.3-Flash is the stronger pick. Note that non-Chinese models generally require overseas network access for their APIs.
Tool use & ecosystem
Function-calling score: GLM-5.3-Flash 70 vs MiniMax M2.7 72, so MiniMax M2.7 has the edge on structured tool use. Fine-tuning is available from GLM-5.3-Flash and MiniMax M2.7. Factor in existing SDK/plugin familiarity — switching cost often outweighs a few-point tool-use gap.
Cost worked example — same workload, real token math
Assume a production workload of 100M input + 30M output tokens per month, with 90% of input tokens served from cache. Figures use public list prices.
| Scenario · per month | GLM-5.3-Flash | MiniMax M2.7 | Gap |
|---|---|---|---|
| List priceno cache applied | $15100M in × $0.07 + 30M out × $0.25 | $59100M in × $0.27 + 30M out × $1.08 | 3.93×gap |
| With caching90% of inputs cache-hit | $15no published cache discount | $59no published cache discount | 3.93×gap |
Illustrative model; your input/output mix and cache-hit ratio change the result. Prices are list rates before any enterprise agreement.