DeepSeek V4.1 Flash vs GLM 5.3 Flash
Close to a tie overall. GLM 5.3 Flash costs 21% less for the same work, and both read a 1M-token context. Through FullsendAI, DeepSeek V4.1 Flash starts answering about 3.2× sooner.
DeepSeek V4.1 Flash
by DeepSeek · #11 of 67 overall
- Context
- 1M
- Input / 1M
- $0.20
- Output / 1M
- $0.60
GLM 5.3 Flash
by Z.ai · #13 of 67 overall
- Context
- 1M
- Input / 1M
- $0.15
- Output / 1M
- $0.50
Better overall
About even
Grade 83.5 vs 82.9
Cheaper
GLM 5.3 Flash
21% less, $0.24 vs $0.30 blended
Larger context
Same
1M vs 1M tokens
Faster first token
DeepSeek V4.1 Flash
393 ms vs 1,249 ms median, through FullsendAI
Head to head
Public benchmarks. Longer is better; each value links to its source.
DeepSeek V4.1 FlashGLM 5.3 Flashv = reported by the model’s maker. — = no public score yet. Arena bars span the range across every model we grade.
Speed through FullsendAI
Measured through FullsendAI, from one location, Oct 15, 2026, n=20 (5 for long runs).
DeepSeek V4.1 FlashGLM 5.3 Flash
Time to first tokenmedian, tick at p90 · lower is faster
Output speed1,500-token replies, tick at p90
First-try success 55/55 for DeepSeek V4.1 Flash and 47/55 for GLM 5.3 Flash. FullsendAI retries busy responses automatically, so a missed first try costs a moment, not an error.
How we measured
Streamed chat completions with default settings, the two models alternating run by run. Time to first token runs from sending the request to the first streamed token, connection included, over short prompts. Output speed is output tokens a second after the first token, on long replies. One location and one afternoon, so treat small differences as noise.
What it costs you
Drag to match your monthly usage.
GLM 5.3 Flash saves you $14.00 a month at this usage, $168 a year.
Capabilities
| Capability | DeepSeek V4.1 Flash | GLM 5.3 Flash |
|---|---|---|
| Reasoning levels through FullsendAI | Yes | Yes |
| Reads images through FullsendAI | Yes | Yes |
| Open weights | Yes | Yes |
| Tool calling | Yes | Yes |
| JSON output | Yes | Yes |
| Tool calls that worked (measured) | 10/10 | 10/10 |
| Valid JSON replies (measured) | 10/10 | 10/10 |
Our coding quick check (ten small Python functions against hidden tests; not a benchmark): 10/10 for DeepSeek V4.1 Flash, 10/10 for GLM 5.3 Flash.
Maker-published benchmarks
As each maker reports them, with their own harnesses and settings: not graded, and not strictly comparable across makers.
| Published by both | DeepSeek V4.1 Flash | GLM 5.3 Flash |
|---|---|---|
| Terminal-Bench 2.1 | 90.6% | 84.3%chart |
| DeepSWE v1.1 | 74.2% | 63.4% |
| AutomationBench | 54.8% | 48.8% |
| Agent's Last Exam | 31.8% | 26.3%chart |
Published by one maker only (13)
| Benchmark | DeepSeek V4.1 Flash | GLM 5.3 Flash |
|---|---|---|
| Terminal-Bench 3.0 | 30% | — |
| Terminal-Bench 4.0 | 31.2% | — |
| NL2Repo-Bench | 64 | — |
| ProgramBench | 20.3% | — |
| Codeforces | 3,471 rating | — |
| SEC-Bench Pro | 62.8% | — |
| CyberGym | 88.1% | — |
| ExploitGym | 15.3% | — |
| HumanEval (base model) | 79.4% | — |
| BigCodeBench (base model) | 60.6% | — |
| Z.ai Code Bench v1.0 (in-house) | — | 29 |
| HLE w/ tools | — | 55.3%chart |
| GDPval-AA v2 | — | 1,773 Elochart |
Each score links to where its maker published it. “chart” marks a number read from a chart image.
At a glance
| Attribute | DeepSeek V4.1 Flash | GLM 5.3 Flash |
|---|---|---|
| Made by | DeepSeek | Z.ai |
| Availability | Coming soon | Coming soon |
| Overall grade | Excellent · 83.5 | Excellent · 82.9 |
| Overall rank | #11 of 67 | #13 of 67 |
| Input price | $0.20 per 1M | $0.15 per 1M |
| Output price | $0.60 per 1M | $0.50 per 1M |
| Context window | 1,048,576 tokens | 1,048,576 tokens |
| Reasoning levels | Yes | Yes |
| Reads images | Yes | Yes |
| Model ID | deepseek-v4.1-flash | glm-5.3-flash |
Model card: what the makers publish
| Fact | DeepSeek V4.1 Flash | GLM 5.3 Flash |
|---|---|---|
| Released | Sep 10, 2026 | Aug 26, 2026 |
| Made by | DeepSeek (DeepSeek-AI) | Z.ai (zai-org; Zhipu AI) |
| Licence | MIT License (repository and model weights) | MIT License |
| Commercial use | Allowed (MIT License permits use, modification, distribution, sublicensing and sale) | Allowed (MIT License) |
| Open weights | Yes | Yes |
| Parameters | 552B backbone parameters (includes 196B Engram conditional-memory parameters per the card's architecture notes) | 320B |
| Active parameters | 8B per token during prefill / 16B per token during decode | 18B |
| Mixture of experts | Yes: 1 shared + 384 routed experts per MoE layer, 6 routed experts active per token | Yes: 288 routed experts + 1 shared, 8 experts per token, first 3 layers dense |
| Architecture | Causal Encoder-Decoder (CED): 40-layer Transformer = 20-layer causal encoder + 20-layer decoder; decoder global KV projected from final encoder states. Compressed Sparse Attention 2 (CSA2, Full/Reindex/Reuse layer modes, Hierarchical Sparse Indexer), FP4 main KV cache (E2M1, one E4M3 scale per 16 channels), ~890 bytes/token global KV cache (~1/4 of DeepSeek-V4-Flash), SWA Bounded Replay, Single-Pass mHC, Engram conditional memory (196B params), DSpark speculative decoding, DeepSeek-ViT vision encoder + 2-layer MLP projector. | Hybrid sparse + linear attention (linear attention for local dependencies, sparse attention with a lightweight indexer for global context; IndexPool compresses 4 indexer keys into 1), Manifold-Constrained Hyper-Connections (mHC), MTP layer, ViT vision encoder; 45 layers. Z.ai: attention compute and KV cache reduced 3.01x and 4.44x vs GLM-5.3. |
| Context window | 1,048,576 tokens | 1,048,576 tokens |
| Max output | 384K (DeepSeek's own API, 'MAXIMUM: 384K') | 128K (Z.ai API) |
| Input | Text, Image | Text, Image, Video, File |
| Output | Text | Text |
| Reasoning | Thinking mode with continuously controllable reasoning effort (integer 1-100). DeepSeek API: thinking on by default (effort 'high'); toggle via {"thinking": {"type": "enabled"|"disabled"}}, effort via reasoning_effort low/high/max. In thinking mode temperature/penalties are ignored; reasoning returned in reasoning_content. | Thinking with reasoning_effort low/high/max (default max if omitted). On Z.ai's API thinking is forced (thinking.type only supports 'enabled'; cannot be disabled). Chat template clear_thinking defaults to false; pass clear_thinking=true for chat. Interleaved and preserved thinking supported. |
| Tool calling | Yes | Yes |
| JSON output | JSON Output via response_format {'type':'json_object'} (prompt must mention 'json') | Structured output / JSON mode via response_format {'type':'json_object'} |
| Recommended sampling | temperature 1.0, top_p 0.95 or 1.0, context_window 1M, max_tokens >= 256K | temperature 1, top_p 0.95, reasoning_effort max; thinking.clear_thinking false; stream + tool_stream for streaming |
| Training data | Pre-trained from scratch on a 45T-token multimodal corpus; context extended to 1M at 34T tokens | Newly trained base model; 30T-token multimodal pre-training corpus |
| Other features | Chat Prefix Completion (Beta), FIM Completion (Beta, non-thinking mode only), Responses API and Anthropic-format API on DeepSeek's own endpoint; context caching (cache-hit pricing); vision | Context caching; visual coding loop (screenshots/recordings to apps), computer use; GLM-5.3-FlashX variant at ~200 tokens/s on Z.ai; MTP layer (speculative decoding) in architecture |
| Runs locally with | — | SGLang, vLLM, TokenSpeed, Transformers, KTransformers, Unsloth |
Sources
- Released: DeepSeek V4.1 Flash, GLM 5.3 Flash
- Made by: DeepSeek V4.1 Flash, GLM 5.3 Flash
- Licence: DeepSeek V4.1 Flash, GLM 5.3 Flash
- Commercial use: DeepSeek V4.1 Flash, GLM 5.3 Flash
- Open weights: DeepSeek V4.1 Flash, GLM 5.3 Flash
- Parameters: DeepSeek V4.1 Flash, GLM 5.3 Flash
- Active parameters: DeepSeek V4.1 Flash, GLM 5.3 Flash
- Mixture of experts: DeepSeek V4.1 Flash, GLM 5.3 Flash
- Architecture: DeepSeek V4.1 Flash, GLM 5.3 Flash
- Context window: DeepSeek V4.1 Flash, GLM 5.3 Flash
- Max output: DeepSeek V4.1 Flash, GLM 5.3 Flash
- Input: DeepSeek V4.1 Flash, GLM 5.3 Flash
- Output: DeepSeek V4.1 Flash, GLM 5.3 Flash
- Reasoning: DeepSeek V4.1 Flash, GLM 5.3 Flash
- Tool calling: DeepSeek V4.1 Flash, GLM 5.3 Flash
- JSON output: DeepSeek V4.1 Flash, GLM 5.3 Flash
- Recommended sampling: DeepSeek V4.1 Flash, GLM 5.3 Flash
- Training data: DeepSeek V4.1 Flash, GLM 5.3 Flash
- Other features: DeepSeek V4.1 Flash, GLM 5.3 Flash
- Runs locally with: GLM 5.3 Flash
Try both with one key
Same endpoint, same key, both hosted in the USA. Change one word to switch models, in Claude Code, Cursor, Cline or your own code.
Get your API key# Ask either model the same thing curl https://fullsendai.com/v1/chat/completions \ -H "Authorization: Bearer $FULLSEND_API_KEY" \ -d '{"model": "deepseek-v4.1-flash", "messages": [...]}' # …then swap one word -d '{"model": "glm-5.3-flash", "messages": [...]}'
Questions
Is DeepSeek V4.1 Flash or GLM 5.3 Flash better?
They grade within two points of each other (83.5 and 82.9), so pick on price and the benchmarks that match your work.
Which is cheaper, DeepSeek V4.1 Flash or GLM 5.3 Flash?
GLM 5.3 Flash: $0.15 in and $0.50 out per million tokens, against $0.20 and $0.60 through FullsendAI.
Which has the larger context window?
Neither: both take 1,048,576 tokens.
Which one is faster?
Measured through FullsendAI, DeepSeek V4.1 Flash started answering sooner: 393 ms median to the first token for DeepSeek V4.1 Flash, against 1,249 ms for GLM 5.3 Flash.
Can I use both with one API key?
Yes, once they launch: both are coming soon, at the prices shown, and both will use the same FullsendAI key.
Compare with others
Scores: LMArena (CC BY 4.0), Terminal-Bench, and model makers’ published results where marked v. Prices are FullsendAI’s, per million tokens. Data as of Oct 28, 2026.