GLM 5.3 FlashX vs Qwen3.6 27B
GLM 5.3 FlashX costs 2.2x less per output token than Qwen3.6 27B ($1.25 vs $2.70 per million). Updated October 22, 2026; no benchmark has tested both models yet.
- Coding
- No data
- Overall
- No data
- Output $/M
- $1.25
- Coding
- 36.8
- Overall
- 45.3
- Output $/M
- $2.70
GLM 5.3 FlashX or Qwen3.6 27B: which to pick
Only Qwen3.6 27B has a coding score, 36.8 on the Vibe Coding Index; GLM 5.3 FlashX has none, so the two cannot be ranked on coding.
Pick GLM 5.3 FlashX for output tokens 2.2x cheaper ($1.25 vs $2.70 per million) and a larger context window (1.05M against 262K tokens).
No benchmark has tested both yet. GLM 5.3 FlashX has none yet; Qwen3.6 27B on AA Coding Index, SWE-bench Verified (Vals AI) and Vibe Code Bench (Vals AI). 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.
Coding
Vibe Coding Index, six public coding benchmarks
No call
Only Qwen3.6 27B has a coding score, so no call is made.
One side not measured
Intelligence
AA Intelligence Index
No call
Only Qwen3.6 27B has a AA Intelligence Index result, 21.4.
Directional only
Agentic work
AA Agentic Index
No call
Only Qwen3.6 27B has a AA Agentic Index result, 18.5.
Directional only
Long documents
Listed context window
GLM 5.3 FlashX
GLM 5.3 FlashX takes 1.05M tokens against 262K.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
GLM 5.3 FlashX
GLM 5.3 FlashX $0.0010, Qwen3.6 27B $0.0018 per turn.
List prices
Cost per solved task
One SWE-bench Verified task, solved
No call
Only Qwen3.6 27B has the number, $1.30.
Directional only
At a glance
- Coding index
- GLM 5.3 FlashX not benchmarked, Qwen3.6 27B 36.8 from 2 benchmarks.
- 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 5.3 FlashX $1.25 against Qwen3.6 27B $2.70 per million output tokens, 2.2x apart.
- Context window
- GLM 5.3 FlashX takes 1.05M tokens, Qwen3.6 27B 262K.
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 Index5 boards measured
| Benchmark | GLM 5.3 FlashX | Qwen3.6 27B | Leader |
|---|---|---|---|
| AA Coding Index | Not measured | 53.7 | Only Qwen3.6 27B measured |
| SWE-bench Verified (Vals AI) | Not measured | 70.0% | Only Qwen3.6 27B measured |
| Vibe Code Bench (Vals AI) | Not measured | 11.9% | Only Qwen3.6 27B measured |
| SciCode | Not measured | 42.8% | Only Qwen3.6 27B measured |
| Terminal-Bench Hard (Artificial Analysis run) | Not measured | 34.8% | Only Qwen3.6 27B measured |
| Neither model has a result on the other 34 boards here | |||
IntelligenceDirectional onlyPublic boards, shown as published, kept out of the index2 boards measured
| Benchmark | GLM 5.3 FlashX | Qwen3.6 27B | Leader |
|---|---|---|---|
| AA Intelligence Index | Not measured | 21.4 | Only Qwen3.6 27B measured |
| Epoch Capabilities Index | Not measured | 146.2 | Only Qwen3.6 27B measured |
Agentic workDirectional onlyPublic boards, shown as published, kept out of the index2 boards measured
| Benchmark | GLM 5.3 FlashX | Qwen3.6 27B | Leader |
|---|---|---|---|
| AA Agentic Index | Not measured | 18.5 | Only Qwen3.6 27B measured |
| τ²-Bench Telecom | Not measured | 94.2% | Only Qwen3.6 27B measured |
| Neither model has a result on the other 39 boards here | |||
ReasoningDirectional onlyPublic boards, shown as published, kept out of the index4 boards measured
| Benchmark | GLM 5.3 FlashX | Qwen3.6 27B | Leader |
|---|---|---|---|
| GPQA Diamond (Artificial Analysis run) | Not measured | 84.2% | Only Qwen3.6 27B measured |
| Humanity's Last Exam (Artificial Analysis run) | Not measured | 23.1% | Only Qwen3.6 27B measured |
| GPQA Diamond (Epoch AI run) | Not measured | 85.9% | Only Qwen3.6 27B measured |
| Mystery game puzzles | Not measured | 7.0% | Only Qwen3.6 27B measured |
| Neither model has a result on the other 18 boards here | |||
MathDirectional onlyPublic boards, shown as published, kept out of the index2 boards measured
| Benchmark | GLM 5.3 FlashX | Qwen3.6 27B | Leader |
|---|---|---|---|
| OTIS Mock AIME | Not measured | 91.1% | Only Qwen3.6 27B measured |
| FrontierMath, tiers 1 to 3 | Not measured | 35.1% | Only Qwen3.6 27B measured |
| Neither model has a result on the other 11 boards here | |||
KnowledgeDirectional onlyPublic boards, shown as published, kept out of the index4 boards measured
| Benchmark | GLM 5.3 FlashX | Qwen3.6 27B | Leader |
|---|---|---|---|
| CaseLaw (Vals AI) | Not measured | 53.2% | Only Qwen3.6 27B measured |
| CorpFin (Vals AI) | Not measured | 62.3% | Only Qwen3.6 27B measured |
| TaxEval (Vals AI) | Not measured | 71.3% | Only Qwen3.6 27B measured |
| MortgageTax (Vals AI) | Not measured | 68.3% | Only Qwen3.6 27B measured |
| Neither model has a result on the other 14 boards here | |||
GeneralDirectional onlyPublic boards, shown as published, kept out of the index5 boards measured
| Benchmark | GLM 5.3 FlashX | Qwen3.6 27B | Leader |
|---|---|---|---|
| SAGE (Vals AI) | Not measured | 45.6% | Only Qwen3.6 27B measured |
| IFBench | Not measured | 67.6% | Only Qwen3.6 27B measured |
| Long context reasoning (AA-LCR) | Not measured | 77.3% | Only Qwen3.6 27B measured |
| DTBench | Not measured | 78.1% | Only Qwen3.6 27B measured |
| LMCA | Not measured | 34.5% | Only Qwen3.6 27B measured |
| Neither model has a result on the other 41 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 5.3 FlashX costs less
Repository review
50K input + 3K output tokens
GLM 5.3 FlashX costs less
Agent loop
200K input + 10K output tokens
GLM 5.3 FlashX 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.
| Specification | Z.ai GLM 5.3 FlashX | Qwen3.6 27B |
|---|---|---|
| Maker | Z.ai | Qwen |
| Weights | No public weights listed | Open weights |
| Released | September 18, 2026 | April 27, 2026 |
| Context window | 1.05M | 262K |
| Input $/Mtok | $0.37 | $0.45 |
| Output $/Mtok | $1.25 | $2.70 |
| Coding index | Not benchmarked | 36.8 |
| Independent measurements behind it | 0 | 2 |
| Cost per solved task | Not computable | $1.30 |
| Source | OpenRouter listing | OpenRouter listing |
Questions
Is GLM 5.3 FlashX better than Qwen3.6 27B for coding?
Only Qwen3.6 27B has a coding score, 36.8 on the Vibe Coding Index; GLM 5.3 FlashX has none, so the two cannot be ranked on coding. Pick GLM 5.3 FlashX for output tokens 2.2x cheaper ($1.25 vs $2.70 per million) and a larger context window (1.05M against 262K tokens).
Which is cheaper, GLM 5.3 FlashX or Qwen3.6 27B?
GLM 5.3 FlashX costs $0.37 per million input tokens and $1.25 per million output tokens; Qwen3.6 27B costs $0.45 and $2.70. Cost per solved task is known for Qwen3.6 27B only.
Which has the bigger context window, GLM 5.3 FlashX or Qwen3.6 27B?
GLM 5.3 FlashX, with 1.05M tokens against 262K.
Has any benchmark tested both GLM 5.3 FlashX and Qwen3.6 27B?
No benchmark has tested both yet. GLM 5.3 FlashX has none yet; Qwen3.6 27B on AA Coding Index, SWE-bench Verified (Vals AI) and Vibe Code Bench (Vals AI). Compare them on price and context instead. Data as of October 22, 2026.