new model · 2026.10.18
Last updated
DeepSeek 1017 vs Opus 5 vs GLM-5.3: scores and cost together
Coding favors 1017, overall reasoning favors Opus 5, and local execution splits between 1017 and GLM-5.3-Flash.
On DeepSeek's model card, 1017 leads the September model by a wide margin on coding and agent benchmarks such as Terminal-Bench and DeepSWE, and passes Opus 5 on some of them. Opus 5 still leads broad reasoning rows such as HLE and NL2Repo. Add API1 price differences and whether the weights can run locally, and no single score decides the choice.
The comparison table comes from fixed harness conditions
DeepSeek's model card compares 1017, September V4.1, K3, GLM-5.3, GPT-5.6 Sol and Opus 5 on the same rows. Terminal-Bench 4.0 was measured with the Claude Code harness and DeepSWE v1.1 with Mini-SWE-V2. Different harnesses give different scores for the same model, so the table does not transfer directly to your tooling.
These are also not local speed figures. The scores measure whether a task was completed; tokens2 per second and first-token latency need separate measurement.
1017 leads on coding and agent tasks
On Terminal-Bench 4.0, 1017 scores 42.3, which is 11.1 points above September's 31.2 but below Opus 5 at 51.8. DeepSWE v1.1 reaches 77.0, above Opus 5 at 74.0, while ProgramBench rises from 20.3 to 29.1 but stays under Opus 5 at 37.0.
On agent rows, Automation-Bench reaches 60.5 and Agents' Last Exam 35.4, above both September and Opus 5. These are the model card's published values, not a promise that your repository sees the same result.
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| Benchmark | 1017 | Opus 5 | GLM-5.3 |
|---|---|---|---|
| Terminal-Bench 4.0 | 42.3 | 51.8 | 37.9 |
| DeepSWE v1.1 | 77.0 | 74.0 | 66.9 |
| ProgramBench | 29.1 | 37.0 | 19.0 |
| HLE (without tools) | 42.6 | 56.3 | 42.0 |
Broad reasoning still favors Opus 5
HLE without tools rises from 36.8 to 42.6, still under Opus 5 at 56.3 and GPT-5.6 Sol at 44.5. NL2Repo-Bench is 68.2 against Opus 5 at 75.3. On visual and tool-assisted rows such as Chartography and BabyVision, however, it has come close to Opus 5.
A claim that 1017 beat Opus 5 is true on one coding row, not as a conclusion about overall capability. The choice depends on the tasks you actually run.
The price gap is larger than the score gap
On the DeepSeek API, deepseek-flash charges $0.3 at peak for uncached input and $1.2 for output. Opus 5 is listed at $5 input and $25 output. For the same tokens that is about 17 times more on input and 21 times on output; DeepSeek's off-peak rates halve again, widening the gap.
Agent work tends to produce long output. A task using 20,000 input and 10,000 output tokens costs about $0.018 on DeepSeek at peak and about $0.35 on Opus 5. Shorter outputs in 1017 can widen that per-task gap further, but retries for failed tasks belong in the calculation.
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| Model | Input | Output | Weights |
|---|---|---|---|
| deepseek-flash (peak) | $0.3 / 1M | $1.2 / 1M | MIT |
| Claude Opus 5 | $5 / 1M | $25 / 1M | Closed |
| GLM-5.3-Flash | Provider-specific | Provider-specific | Open |
The local options are 1017 and GLM-5.3-Flash
1017 publishes 475.25 GiB of official files under the MIT license, and GLM-5.3-Flash is an open-weight model with 320B total and 18B active parameters. Opus 5 has no public weights and is API-only. If local execution is a requirement, the choice narrows to 1017 and GLM-5.3-Flash.
With a 1M context and a 552B backbone, 1017 on personal hardware needs compressed conversions and SSD3 streaming. GLM-5.3-Flash is also 320B and needs server-class memory. Open weights do not mean it runs on your laptop.
Three checks that decide it for your work
First, pick three tasks you run often and compare them with the same prompts4. Second, record the tokens and retries each one needed. Third, confirm whether the data may leave your network. These three checks matter more than the score table.
Rather than chase a one-point benchmark difference, choose the option with a lower failure rate and predictable cost on your tasks. In short: 1017 for coding, agents and local execution; Opus 5 for broad reasoning and a managed API; GLM-5.3-Flash as another open-weight option.
Terminology notes
API — A defined interface that lets other code call a program’s functions.
Back to the textToken — A unit into which a model divides input or output for processing.
Back to the textSSD — A data storage device that uses flash memory and retains data without power.
Back to the textPrompt — The input text sent to a model. For generative models it describes the desired result and strongly shapes the output.
Back to the text
Frequently asked questions
- What should you check first in this article?
- On DeepSeek's model card, 1017 leads the September model by a wide margin on coding and agent benchmarks such as Terminal-Bench and DeepSWE, and passes Opus 5 on some of them. Opus 5 still leads broad reasoning rows such as HLE and NL2Repo. Add API price differences and whether the weights can run locally, and no single score decides the choice.
- How much memory does it need?
- With a 1M context and a 552B backbone, 1017 on personal hardware needs compressed conversions and SSD streaming. GLM-5.3-Flash is also 320B and needs server-class memory. Open weights do not mean it runs on your laptop.